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Mastering the Exam PL-500: Microsoft Power Automate RPA Developer

Satish Prasad — Wed, 23 Aug 2023 05:58:19 +0000

Dive deep into the world of Exam PL-500: Microsoft Power Automate RPA Developer. Discover insights, tips, and comprehensive knowledge to ace this certification.

Introduction

The world of automation is evolving, and Microsoft is at the forefront with its Power Automate RPA Developer certification. The Exam PL-500 is not just another test; it’s a testament to one’s skills, knowledge, and expertise in the realm of Robotic Process Automation (RPA).

Let’s embark on a journey to understand this exam inside and out.

Exam PL-500: Microsoft Power Automate RPA Developer Deep Dive

The Exam PL-500: Microsoft Power Automate RPA Developer is designed for candidates who aim to automate various applications, including Windows-based, browser-based, and terminal-based, that are either time-consuming or consist of repetitive processes. These candidates utilize a combination of methods such as UI, API, and database automation.

The primary responsibilities of these candidates include analyzing and designing processes suitable for automation. They implement this automation using both Power Automate for desktop and Power Automate cloud flows. RPA developers employ actions for logic and to interact with data, applications, and services. They collaborate with business stakeholders to optimize business workflows and work alongside administrators to deploy solutions across different environments and provide support for these solutions.

Candidates aiming for this certification should possess experience with the Windows desktop environment, scripting languages, Power Automate cloud and desktop flows, AI Builder, Process Advisor, and Microsoft Dataverse.

It’s important to note that the English version of this exam underwent updates on June 27, 2023. The study guide linked provides details about these changes. The exam primarily measures a candidate’s ability to design, develop, deploy and manage automations.

The exam is available in multiple languages, including English, Chinese (Simplified), German, Spanish, French, Italian, Japanese, and Portuguese (Brazil). The passing score for the exam is 700.

Skills Measured:

Design automations (30–35%)
Develop automations (40–45%)
Deploy and manage automations (20–25%)

Training Resources: Several learning paths are available to help candidates prepare for the exam. These include modules on automating processes with Robotic Process Automation and Power Automate for desktop, getting started with Power Automate for desktop, working with different technologies in Power Automate for desktop, and implementing advanced logic in Power Automate for desktop.

Related Certifications: Successful completion of this exam leads to the certification: Microsoft Certified: Power Automate RPA Developer Associate.

For more details and resources, candidates can visit the official Microsoft Certification page.

Key Modules in the Exam

The exam is structured into various modules, each focusing on a specific aspect of Power Automate. These include understanding RPA, designing and building Power Automate flows, integrating with other Microsoft services, and more.

Preparing for the Exam

Preparation is the key to success. Start with the basics, understand the exam pattern, and gradually delve into the advanced topics. Utilize online resources, join study groups, and don’t hesitate to seek expert advice.

Tips and Tricks for Success

From time management to choosing the right study material, there are several strategies that can help you ace the exam. Remember, practice makes perfect. So, take mock tests, analyze your weak points, and work on them.

Common Mistakes to Avoid

While many candidates focus on covering the entire syllabus, they often overlook the importance of revision or get bogged down by unnecessary details. Avoiding such pitfalls can be the difference between success and failure.

Benefits of Being a Certified RPA Developer

A certification not only adds to your resume but also opens doors to numerous opportunities. Companies today are looking for certified professionals who can drive their automation initiatives.

Career Opportunities Post-Certification

From being an RPA developer to leading automation projects, the opportunities post-certification are vast and rewarding. The demand for RPA professionals is on the rise, and with a certification, you’re ahead of the curve.

Real-world Applications of Power Automate

From automating invoice processing to streamlining HR tasks, Power Automate is making waves in the real world. Businesses are leveraging its capabilities to drive efficiency and innovation.

Comparing Power Automate with Other RPA Tools

While Power Automate is a powerful tool, there are other players in the market. How does it stack up against them? Let’s delve into a comparative analysis.

The Future of RPA and Power Automate

The world of RPA is evolving, and with AI and ML integration, the future looks promising. Power Automate, with its continuous upgrades, is poised to be a leader in this revolution.

Personal Experiences with Exam PL-500

Hearing from those who’ve walked the path can provide invaluable insights. Here, we share some first-hand experiences of professionals who’ve aced Exam PL-500.

Expert Interviews on Power Automate

What do the experts have to say about Power Automate and its potential? We’ve curated some insightful interviews that shed light on the future of RPA and Power Automate.

Resources for Further Learning

The learning never stops. Here’s a list of resources, from books to online courses, that can help you deepen your knowledge and stay updated in the world of RPA.

FAQ Microsoft Power Automate RPA Developer

What is the Exam PL-500 all about? The Exam PL-500 is a certification test for Microsoft Power Automate RPA Developer. It assesses an individual’s skills and knowledge related to RPA and Power Automate.
How can I prepare for the exam? Start with understanding the syllabus, utilize online resources, join study groups, and take mock tests for practice.
Is the certification worth it? Absolutely! The certification not only adds value to your resume but also opens doors to numerous career opportunities in the field of RPA.
How does Power Automate compare with other RPA tools? Power Automate is a robust tool with a user-friendly interface. While there are other RPA tools in the market, Power Automate stands out due to its integration capabilities with other Microsoft services.
What are the career opportunities post-certification? From being an RPA developer to leading automation projects, the possibilities are vast and rewarding.
What’s the future of RPA and Power Automate? The RPA industry is evolving rapidly, and with the integration of AI and ML, the future looks promising. Power Automate, with its continuous upgrades, is set to be a significant player in this evolution.

Conclusion

The Exam PL-500: Microsoft Power Automate RPA Developer is not just a test; it’s a journey into the world of automation. With the right preparation and mindset, you can ace this exam and pave the way for a rewarding career in RPA. The future is automated, and with tools like Power Automate, the possibilities are endless.

References

Microsoft Certified: Power Platform Fundamentals – Certifications | Microsoft Learn

Exam PL-500: Microsoft Power Automate RPA Developer – Certifications | Microsoft Learn

AI Builder in Power Automate: Harnessing the Power of AI- A Comprehensive Guide

Satish Prasad — Fri, 07 Jul 2023 11:35:54 +0000

In today’s digital era, businesses are constantly seeking ways to automate their processes and streamline their operations. One powerful tool that has gained significant popularity is the AI Builder in Power Automate. With its advanced capabilities and user-friendly interface, the AI Builder in Power Automate allows users to incorporate artificial intelligence into their automation workflows seamlessly.

Can you imagine a world where automation goes beyond simple tasks and steps into the realm of AI-driven decision-making? If so, welcome to the world of AI Builder in Power Automate. But, what exactly is it? Why is it such a game-changer?

Let’s find out!

Introduction to AI Builder in Power Automate

Artificial Intelligence (AI) has transformed how we view and interact with technology. It’s changed the way we work, the way we communicate, and the way we live our lives. It’s redefined how we process information and make decisions. One application of AI that is revolutionizing the world of workflow automation is the AI Builder in Power Automate.

AI Builder is Microsoft’s incredible artificial intelligence (AI) tool that provides a no-code AI experience. In other words, you don’t have to be an AI expert to use it. Pretty neat, right?

Understanding Power Automate

Now, let’s talk about Power Automate. Formerly known as Microsoft Flow, Power Automate is a service that helps businesses create automated workflows between apps and services to synchronize files, get notifications, collect data, and much more. Think of it as your productivity-boosting sidekick!

Power Automate, At its core, it’s about simplifying repetitive tasks and integrating various services and applications. Power Automate offers a user-friendly, no-code approach to workflow automation, opening the door for non-technical users to automate and optimize business processes.

Power Automate boasts an extensive suite of features. It allows you to create automated workflows between your favourite apps and services, automate repetitive tasks without coding or relying on developers, and turn complex business problems into streamlined processes.

AI Builder: The Game Changer

When AI Builder and Power Automate join forces, it’s like a superhero team-up. The power of AI enhances automated workflows, making them more intelligent, versatile, and dynamic. Sounds like something out of a sci-fi movie, right?

What is AI Builder?

AI Builder is a feature of Power Automate that takes automation to the next level. It’s an AI-powered add-on that empowers users to harness machine learning to make predictions, perform complex analyses, process forms, and more.

AI Builder Capabilities

AI Builder offers capabilities such as prediction, form processing, object detection, and text classification. It has prebuilt and customizable AI models, enabling you to tailor the tool to your business’s specific needs.

Key Features of AI Builder in Power Automate

Prebuilt AI Models

One key feature of AI Builder is its prebuilt models. This includes things like form processing, object detection, and prediction, all designed to make your life easier. It’s like having a ready-to-cook meal; you just have to heat it up!

Prebuilt AI models are already designed and can be implemented without the need for further training.

Business Card Reader model: Extracts relevant information from business card images like name, job title, address, email, etc.

Text Recognition model: Helps to convert words from images and documents into a machine-readable format, including keyword extraction from documents.

Category Classification model: Classifies entire text into categories using different tags. Pre-built models exist for areas such as customer feedback, including issues, complaints, customer service, documentation, price & billing, and staff.

Key Phrase Extraction model: Extracts main points or keyphrases from text. For example, from the sentence “The restaurant was good, and there was wonderful staff,” it would extract “restaurant” and “wonderful staff.”

Language Detection model: Identifies the language of a text document, returning the language code (e.g., “en” for English). If the language cannot be detected, it returns ‘unknown’.

Sentiment Analysis model: Detects positive, negative, neutral, and mixed sentiments in text.

Custom AI Models

If the prebuilt models don’t cater to your needs, don’t worry! AI Builder lets you build custom AI models. You’re the chef in this kitchen, designing the AI experience you want.

Prediction model: This model enables predictions of future outcomes, such as identifying potential exam candidates, by analyzing past data.

Form processing model: This utilizes machine learning technologies to extract key-value pairs from documents and images, such as names, titles, and invoice amounts. A minimum of five documents is required to initiate this process.

Object detection model: Aids in automating or accelerating business processes. It can significantly contribute to areas like inventory management and customer relationship management in retail.

Category classification model: Helps analyze and act upon large volumes of text data from various sources like email, social media, or documents. It tags different types of text entries, useful in tasks such as spam detection and customer request routing.

Entity extraction model: Recognizes key elements, classifies them into predefined categories, and transforms unstructured data into structured, machine-readable data.

Seamless Integration

AI Builder is like a friendly neighbour that gets along with everyone. It integrates seamlessly with other Microsoft services, making your job more convenient and efficient.

Exploring AI Builder in Power Automate

How to Use AI Builder in Power Automate

Step 1: Access AI Builder

To kick things off, access the AI Builder from the left-side navigation panel in Power Automate. It’s as easy as clicking a button!

Step 2: Choose a Model

Next, choose a prebuilt model or build a custom one. It’s like selecting a recipe based on what you fancy.

Step 3: Train your AI model

Train AI models on how to resolve your business problem (for example, how to recognize your products on an image) based on your business data and tailoring. When trained, your AI model can generate insights such as the result of a prediction, or the list and number of objects detected in an image.

Step 4: Apply to Power Automate

Lastly, apply the chosen model to your Power Automate workflows. And voila! You’ve just made your workflows smarter.

Step-by-Step Guide to Using AI Builder in Power Automate

Before you can use AI Builder in Power Automate, you need:

Prerequisites: AI Builder requires Microsoft Dataverse, the data platform for Microsoft Power Platform, to store and manage business data. If you’re a Dynamics 365 customer, your data is already in Dataverse. Additionally, AI Builder must be enabled for your environment.

Deploy sample apps and data: To explore AI Builder, users can enable the setting “Deploy sample apps and data” when creating their environment. This adds sample data automatically, but sample data can also be downloaded and uploaded manually.

Getting started: AI Builder allows the creation of models based on data type (e.g., documents, text, structured data, images) and builds types. The custom build type lets users build, train, and publish a model unique to their business. The prebuilt type is ready for use and offers common business scenarios.

Example Working: Extract data from an invoice document and save it in an Excel

Setting up AI Builder

Setting up AI Builder involves a few simple steps. First, you need to ensure you have a Power Automate account and the necessary permissions to use AI Builder. Once logged in, you can navigate to AI Builder from the Power Automate dashboard and select the model you want to use or create a new one.

For this example – Extract information from invoices- The model is fine-tuned to identify common invoice components such as invoice ID, customer details, vendor details, shipping and billing information, total, tax, subtotal, line items, and more.

Next – Add sample documents for your model to study. Put similar documents into the same collection.

Creating Collections: If you have fewer than fifty documents, create only one collection. If not, include five or more documents with similar layouts per collection.

File Types and Size: Supported file types include .JPG, .PNG, and .PDF, with a total size limit of 50 MB or 500 pages.

Purpose of Collections: A collection is a group of documents that share similar layouts and wording. It allows for the creation of a unique AI model that can extract the same information from various documents.

AI Builder calculates the accuracy score for your model based on the prediction result of the test dataset. Before training, AI Builder separates your dataset into separate training data and testing data sets. And after training, AI Builder applies your AI model to the testing dataset to calculate the accuracy score.

This model correctly predicted 81% of actual results so the next step is to Publish the Model for Prediction.

Once published you should be able to use “Your Custom Trained Model” in

Power Automate
Power Apps

You can also create Document automation that combines robotic process automation (RPA) and artificial intelligence, to help you extract and interpret data from structured and unstructured documents to automate your document workflow.

Building Flows with AI Builder

Building flows with AI Builder is a straightforward process. You start by choosing a trigger, which is an event that initiates your flow. You then add actions, which are tasks your flow will perform. With AI Builder, these actions can include AI tasks like prediction or form processing.

Real-World Applications of AI Builder in Power Automate

First, you need to create and train your category classification custom model in AI Builder. This involves collecting a dataset of customer feedback and labelling each feedback with the appropriate category. You can use the AI Builder interface to upload the dataset, annotate the data with category labels, and train the model using the provided tools.

Collect and label a dataset of customer feedback for category classification.

Create a new flow in Power Automate triggered by new customer feedback form submissions.

Add the “Predict” action from AI Builder to the flow.

Configure the input by mapping fields from the feedback form to the model’s input fields.

Handle the output by using conditional statements to route feedback based on the predicted category.

Test the flow with sample feedback forms to ensure accurate predictions.

Refine the model by providing additional training data or iterating on the training process in AI Builder.

By following these steps, you can utilize a category classification custom model in Power Automate to automatically classify customer feedback into different categories. This enables you to efficiently process and route the feedback to the relevant teams, leading to improved customer satisfaction and more effective issue resolution.

Remember to customize the example based on your specific requirements and adjust the flow actions accordingly.

How AI Builder is Revolutionizing Industries

By automating complex tasks and providing predictive capabilities, AI Builder is revolutionizing various industries. It’s enhancing efficiency, reducing costs, and enabling businesses to leverage data innovatively. From healthcare and manufacturing to retail and finance, AI Builder is transforming how businesses operate.

Business Process Automation

Ever wished to automate tedious business processes? With AI Builder in Power Automate, you can! It’s like having a virtual assistant taking care of repetitive tasks, freeing up your time to focus on strategic initiatives.

Data Processing and Analysis

AI Builder can read, extract, and analyze data, providing valuable insights. It’s like having a sharp-eyed detective digging up important clues from a sea of information.

Customer Engagement

AI Builder can analyze customer data and predict behaviour, allowing you to provide personalized experiences. It’s like being able to read your customer’s mind!

Advanced Techniques in AI Builder for Power Automate

Optimizing Workflows with AI Builder

To optimize your workflows with AI Builder, you can leverage its predictive capabilities to anticipate and address business needs. You can also utilize AI Builder’s analytics features to gain insights into your workflows and identify areas for improvement.

Advanced AI Models in Power Automate

In addition to its prebuilt models, AI Builder also allows users to create custom AI models. This feature enables you to tailor AI Builder’s capabilities to your specific business needs, optimizing your workflows and enhancing your business processes.

Future Prospects of AI Builder and Power Automate

The future prospects of AI Builder in Power Automate are exciting and promising. As AI technology advances and becomes even more integrated with business processes, we can expect AI Builder to introduce more sophisticated capabilities. These developments will likely result in even greater efficiency and effectiveness in workflow automation.

Challenges and Solutions in Implementing AI Builder in Power Automate

While AI Builder brings numerous benefits, it also presents challenges. For instance, there can be a learning curve in understanding how to best use its features. However, Microsoft offers extensive resources to assist users, including tutorials and a community forum.

Another challenge might be data privacy and security, particularly when dealing with sensitive information. Here, it’s important to ensure that the appropriate measures, such as encryption and access controls, are in place to protect your data.

Frequently Asked Questions

1. Can anyone use AI Builder in Power Automate?

Yes, AI Builder is designed for everyone, regardless of their AI expertise.

2. Can I use my own data with AI Builder?

Yes, AI Builder allows you to use your own data to train and create custom AI models.

3. How is AI Builder different from Power Automate?

AI Builder is an AI tool, while Power Automate is a service for creating automated workflows. Together, they provide intelligent automation capabilities.

4. What are some applications of AI Builder in Power Automate?

AI Builder can be used in various scenarios, such as business process automation, data processing and analysis, and customer engagement.

5. Are there any limitations to AI Builder in Power Automate?

While AI Builder offers many benefits, it does have its limitations. However, with ongoing improvements and updates, it continues to become more versatile and powerful.

Conclusion: Transforming Business Processes with AI Builder in Power Automate

In conclusion, AI Builder in Power Automate is a transformative tool that’s reshaping business processes across industries. Its integration with Power Automate provides a powerful, intelligent platform for automating workflows and extracting valuable insights from data. By understanding and harnessing the capabilities of AI Builder, businesses can optimize their processes, make more informed decisions, and ultimately drive success in today’s competitive landscape.

Summary: AI Builder in Power Automate is a tool that brings AI capabilities to automation workflows. Users can utilize prebuilt AI models or create custom models. It seamlessly integrates with Power Automate, allowing for data-driven decisions, automation of tasks, and extraction of insights.

AI Builder in Power Automate brings AI capabilities to automation workflows.

Access to AI Builder may require a separate subscription or licensing.

Prebuilt AI models are available for common use cases such as receipt processing and sentiment analysis.

Custom AI models can be created and trained for unique use cases.

AI Builder integrates with Power Automate, enabling AI capabilities in existing workflows.

Data connection to relevant sources is necessary for the effective use of AI Builder.

AI Builder allows for iterative improvement of AI models over time.

References :

Use AI Builder in Power Automate – Power Automate | Microsoft Learn

AI Builder— Intelligent Automation | Microsoft Power Automate

ServiceNow and UiPath Communication Mining Example: Enhancing ITSM Efficiency

Satish Prasad — Mon, 03 Jul 2023 09:05:28 +0000

Problem Statement: ServiceNow Auto Ticket Triaging

ServiceNow is a popular IT service management (ITSM) platform used by organizations to handle incidents, problems, and change management, among other processes.

The problem arises due to the large volume of incoming tickets that an organization receives daily. As the ticket count increases, manual ticket triaging becomes time-consuming and prone to human errors.

The objective is to automate the ticket triaging process using UiPath Communication Mining techniques to improve efficiency, reduce response time, and enhance the overall quality of IT support services.

Solution : Automating ticket triaging with UiPath Communication Mining

Automating ticket triaging with UiPath Communication Mining in ServiceNow streamlines IT support requests, ensuring efficiency and accuracy. Key objectives include analyzing communication sources, extracting relevant data, categorizing and prioritizing tickets, identifying appropriate assignment groups, integrating machine learning, and ensuring seamless ServiceNow integration for real-time processing and continuous improvement.

Communication Source Analysis: The system processes data from various communication channels (email, chat, helpdesk) for a comprehensive view of support requests.

Data Extraction: UiPath Communication Mining extracts vital ticket-related information from the textual content, such as Ticket details, Issue descriptions, timestamps, and metadata.

Ticket Categorization: The system accurately categorizes tickets (incidents, service requests, problems) by understanding context and intent Using UiPath Communication Mining Prediction Labels.

Priority Assignment: Using communication mining, the system analyzes ticket urgency and impact to assign appropriate priority levels, ensuring quick response to critical issues.

Assignment Group Identification: UiPath Communication Mining helps identify the best-suited groups or individuals with the right skills to handle specific ticket categories based on extracted labels and Entity Extraction.

Seamless ServiceNow Integration: The auto-triaging system smoothly integrates with ServiceNow, utilizing UiPath’s Integration Service capabilities to Receive, update, and manage tickets on Real Time Basis.

Working Example: How UiPath Communication Mining will Work

Stage 1: Preparatory Work, Data Collection and Preparation:

1. Access to a UiPath Automation Cloud
2. Access to UiPath Communications Mining instance (please check with your account team to find out how you can get access if you do not already have it).
3. An ITSM Environment of your choice. (for This example ServiceNow) – You should be able to export data from any ITSM Tool.
4. Historical Data Exported from the ITSM Application of your environment
5. UiPath Integration Service Access to get Real-time Tickets
6. UiPath Orchestrator Access to Manage Triggers
7. UiPath Studio to configure Robot and build your automation.

Once you have the required access and the dataset available for Model Training in Communication Mining, The Next step will be setting up the Projects Dataset and Sources.

Let’s get started!

[Dataset Download link and Sample View Provided Below]

            ID_request,Text,Category,Solution,Date_request_recieved,Date_request_solved,ID_agent
4791,"I can't open webpage https://unsplash.com/. I use Chrome, but tried in other net browsers as well. Doesn't help. Please give me the premission to visit any site on the internet.",Discord,"Software updated, PC cleaned.",10/17/2022,10/26/2022,2
3964,
 I need guarantee exchange for a new device. New SN: 30739
 Old SN: 30292,CRM,Asana installed,10/6/2022,10/24/2022,3
2774,"Bundled problems with Office since restart:
Messages not sent
Outlook does not connect, mails do not arrive
Error 0x8004deb0 appears when Connection attempt, see attachment
The company account is affected: AB123
Access via Office.com seems to be possible.",CRM,Problem solved,10/20/2022,10/5/2022,2
4999,This is just test message. Neglect please.,Internet Browser,Problem fixed by connection to users PC.,10/8/2022,10/14/2022,2
2753,My headphones aren't working. I would like to order new.,Internet Browser,Attachment enabled,10/7/2022,10/5/2022,3
4805,My keyboard is broken. Please send the new one,Internet Browser,Software reinstalled,10/10/2022,10/18/2022,2
3769, Die rechte Maustaste funktioniert nicht mehr. Bitte neues schicken. ,Mail,Premission given,10/25/2022,10/18/2022,1
4537,Asana stopped to work after I restarted my PC.,Asana,Discord Installed,10/25/2022,10/17/2022,3
3877,"Avevo asana sul mio PC, ma dopo aver installato la nuova versione di Chrome è scomparsa. Si prega di installare nuovamente Asana sul mio PC.",Help Needed,Outlook reinstalled,10/31/2022,10/14/2022,3
3219,Installation needed.,Outlook,Software reinstalled,10/13/2022,10/13/2022,2
3099,Für den Quartalsbericht benötigen wir dringend die Börsendaten. Ermöglichen Sie den Zugriff auf alle Webseiten so schnell wie möglich. ,Mail,Problem solved,10/17/2022,10/21/2022,2
4999,"Ich kann nicht auf Outlook zugreifen. Es zeigt den Status an, dass es offline arbeitet, und ich bin mit dem Internet verbunden 
 
 Outlook",Internet Browser,Problem fixed by connection to users PC.,10/5/2022,10/14/2022,2
3717,Attachment I got is blocked due to e-mail policy

,Keyboard ,Item sent,10/18/2022,10/21/2022,2
4999,Bitte installieren Sie Asana erneut auf meinem PC. Nach dem letzten Windows#Update funktioniert es nicht mehr.,Help Needed,"Software updated, PC cleaned.",10/24/2022,10/5/2022,3
4999,Why I don't have Discord on the new laptop I ordered when I asked for it? Please install Discord on my pc.,,"Software updated, PC cleaned.",10/3/2022,10/21/2022,3
3618,Outlook funktioniert nach Update nicht. Hilfe bitte,CRM,Item sent,10/17/2022,10/4/2022,2
4999,"Please update Asana on my PC. I have it installed, but new version is available and I can't do the update.",Discord,Attachment enabled,10/24/2022,10/14/2022,3
4693,"Ich kann nicht auf Outlook zugreifen. Es zeigt den Status an, dass es offline arbeitet, und ich bin mit dem Internet verbunden 
 
 Outlook",Internet Browser,Outlook reinstalled,10/6/2022,10/17/2022,1
2603," Ich muss meinen Posteingang mit einem Teammitglied teilen. Berechtigungen, die bereits von name.nachname@firma.com erteilt wurden, finden Sie im beigefügten Screenshot. Das System gibt einen Fehler zurück und auch der Outlook-Client tut dasselbe.",Internet Browser,"Software updated, PC cleaned.",10/21/2022,10/25/2022,2
2372,I would like to order mouse for left-handed person (me).,Mail,Software installed,10/18/2022,10/10/2022,3

Stage 2: Create DataSet, Different Settings for Language and Entity:

Creating a dataset involves combining one or more data sources (up to 20) with a trained model. Sources and datasets can belong to different projects, as long as users have the necessary permissions. When creating a new dataset, the option to copy an existing dataset is available, allowing for the duplication of sources, entities, sentiment selection, labels, and reviewed examples.

GUI or API can set up an empty source with defined properties.

Data can be uploaded through integrations or static CSV upload. (Will Discuss this in the Next Section)

Datasets consist of one or more data sources and a trained model.

Sources and datasets can belong to different projects with appropriate permissions.

Multiple sources in a dataset should serve a similar purpose for analysis or automation.

Option to create a copy of an existing dataset, including its associated elements.

Once created dataset card provides information about a dataset, including the title, description, project linkage, dataset name, connected sources, model family (language), sentiment analysis status, and timestamps for the last change and creation. Additionally, there are options to navigate to Dashboard, Explore, and Reports pages.

Dataset title and description

Project linkage and dataset name

Connected sources – You can choose to create a copy of a pre-existing dataset. This means that you copy over the same sources, entities, sentiment selection, labels and review examples.

Model family (language) – If you wish to set MultiLanguage for your model.

Sentiment analysis status – Here you can set the sentiment of Comment – Once enabled cant be disabled and you need to label it accordingly

Timestamps for the last change and creation

Navigation options: Dashboard, Explore, and Reports

Stage 3: Working with Data Sources, Upload via CSV

To create a data source in the GUI, users with ‘Sources admin’ permissions can follow these steps:

Navigate to the ‘Sources’ page via the Admin console.
Select the ‘New Source’ button at the top of the page.
Fill in the required source properties, such as source project, name, title, description, sensitive properties (with ‘View sensitive data’ permission), language, and translation status (with ‘Create translated sources’ permission).
Click the ‘Create source’ button.

To upload data from a CSV file into a data source, follow these key steps:

1. Navigate to the Sources page via the admin console.
2. Locate the desired data source to upload data into.
3. Click the upload icon in the top right-hand corner of the data source card.
4. Select the CSV file you want to upload.
5. Ensure that the CSV file meets specific criteria:
  - Contains headers on the first line and is delimited by commas or tabs.
  - Requires a minimum of three columns: comment text contents (message), timestamp, and a unique comment ID.
  - Must be encoded as UTF-8, UTF-16, or UTF-32 (automatically detected by the platform).
  - Should be 64 MiB or smaller; larger files can be uploaded by splitting them into multiple files, each less than 64 MiB.

Required Columns in CSV Upload:

- ID column: Unique comment identifier with ASCII alphanumeric characters and certain punctuation.
- Message column: Text of the message for analysis.
- Timestamp column: Date and time of comment recording (flexible format, automatically inferred).

Additional Optional Columns:

- - Subject lines, threads, participants (for cases or email threads).

Note: You might need to perform some cleanup on your data as it may contain invalid characters and your CSV upload might fail.

Here is a list of Common issues :

Not Enough Columns
Invalid Encoding
Invalid Header
Unequal Row Lengths
Id format
Id length
Timestamp Format
Message Length
Number Property Format
Property Length
Unknown Error

Stage 4: Explore Train and Apply Labels

Once you are done with your dataset uploaded to UiPath Communication Mining it will auto-discover and cluster them in different groups (looking similar) and provide you with a guided session for training your data set.

Before diving into Model Training and Applying labels – Let’s understand the key concepts for a better understanding.

Using labels, entities and metadata together

Below is an example verbatim that shows how labels, entities and metadata are distinct but complementary to one another. For this inbound request to be automated, each of them may be required for a specific purpose:

Some Background Details Labels, entities, and metadata

Proper structuring and naming of labels in taxonomy are crucial for effective model training.

Labels should serve a business purpose and provide a useful description of the concept they capture. Labels can have hierarchies, with multiple levels separated by ‘>’.

Each label nested under another should be a subset of the label above it. Additional levels of hierarchy can be added later in the training process.

The model predicts each label independently, and parent labels should capture genuine topics or concepts.

Broad labels should be avoided, as they require more examples to predict well. Splitting broad labels into distinct and specific labels is recommended.

Overview of the model training process

Key Points:

Model training involves iterative steps – The model training process involves iterative steps: Discover, Explore, Refine, and Prune/Re-organize.

Discover clusters and tag labels- In the Discover phase, similar intents and conversation themes are grouped into clusters and tagged with relevant labels.

Explore to train and improve the model – Explore is where the model is further trained by reviewing verbatims, adding labels, and improving understanding.

Refine for overall performance improvement – Refine phase focuses on assessing and improving the model’s overall performance with guided feedback.

Prune/Re-organize for label management – Prune/Re-organize allows renaming, merging, or deleting labels throughout the process.

Key Points to Note :

Applying labels/reviewing entities leads to model retraining

Model computes new predictions with the latest data

Retraining time varies based on factors

Status icons show dataset status

Retraining can occur due to platform/model improvements

Stage 5 :Validation, Report & Pin the Model

The Reports page in the platform provides in-platform reporting on the dataset. Users can access it through the top navigation bar. The reports are filterable to prioritize the most relevant views.

Depending on the data type, there are up to six tabs in the Reports section: Dashboard, Label Summary, Trends, Segments, Threads, and Comparison. Users can switch between verbatim and thread-level reports if the data is in thread form (such as call transcripts or email chains). The verbatim filter is the default if thread-level data is not available.

The functionalities of each tab are as follows:

Dashboard: Users can create custom dashboard views using data from other tabs.

Label Summary: Provides high-level summary statistics for labels.

Trends: Presents charts showing verbatim volume, label volume, and sentiment over a specific time period.

Segments: Shows charts comparing label volumes with verbatim metadata fields, like sender domain.

Threads: Displays charts indicating thread volumes and label volumes within a thread (only accessible with the ‘Thread’ filter applied).

Comparison: Enables users to compare different data cohorts against each other.

Each tab in the Reports page displays the total number of verbatims in the dataset, net sentiment (if sentiment analysis is enabled), and the selected date period. When applying user property, entity, or label filters, these statistics update based on the applied filters and selections.

Every time you train the platform on your data (i.e. labelling any verbatims), a new version of the model associated with your dataset is created. You should be able to see the status and review on the “Train Page”.

Key Points to Note here:

New model versions generated during data training

Previous versions are not automatically stored due to storage constraints

“Pinning” model versions provide prediction determinism

Models can be tagged as ‘Live’ or ‘Staging’ for downstream use

Validation page allows comparing scores of pinned models over time

Stage 6: Using the Communication Mining Model in UiPath Studio Project

Key points to note here:

The platform assigns confidence scores (%) to predictions of labels for inbound communications.

Confidence thresholds determine whether a label applies or not for automatic classification.

Selecting the appropriate threshold achieves the desired balance of precision and recall.

Threshold selection can be done through the Validation page by adjusting the threshold slider or inputting a percentage value.

Precision vs recall chart provides a visual indication of the thresholds that optimize precision, recall, or strike a balance between the two.

Threshold selection depends on the use case, considering the potential consequences of false positives and false negatives.

Examples include maximizing recall for urgent requests to avoid missing them and maximizing precision for high-value transactions to minimize false positives.

To create a stream you will first need to have a Pinned Model.
To create a stream, click the ‘New stream +’ button at the top of the streams page, which will open the ‘Create a stream’ modal.

Get predictions from a pinned model example

Stage 7: Designing End-to-End Automation Using the Trained Model & Other Uipath Components

Highlights:

UiPath Integration Service to get Real-Time Data from ITSM Tool (Service Now) Using ServiceNow Connector

UiPath Orchestrator Triggers Bot based on Events Received from Upstream System ServiceNow Incidents & Service Task

Tickets Description Extracted from Service Now send to UiPath Communication Mining for Labels, Entity extraction using prediction API Call.

Based on Threshold “STP” and “Human in the Loop” feature utilised via UiPath Action Center

Automation Action Based on AI Response in Target system and Update Required details in ServiceNow

Feedback Mechnish is utilised to send data back to UiPath Communication mining using the “Exception” API route.

Summary:

Effective communication mining in IT Service Management (ITSM) offers numerous benefits, including enhanced incident management, improved problem resolution, streamlined change management, boosted knowledge management, and optimized service request management.

Incident Management: Communication mining helps IT teams analyze incident reports and communication logs to identify recurring problems and potential bottlenecks proactively.

Problem Resolution: IT professionals can discover successful solutions from past incidents through communication mining, reducing resolution times and increasing customer satisfaction.

Change Management: Communication mining aids in understanding team communication during change implementation, leading to better coordination and alignment among stakeholders.

Knowledge Management: By analyzing interactions between IT staff, communication mining helps capture tacit knowledge, leading to better decision-making and faster problem-solving.

Service Request Management: Communication mining identifies patterns in service requests, providing insights to improve service delivery and enhance customer experiences.

#UiPath #CommunicationMining #ITSM #IncidentManagement #ProblemResolution #ChangeManagement

Implementing UiPath Communication Mining: Step-by-Step Guide

Satish Prasad — Sun, 02 Jul 2023 17:25:15 +0000

In today’s fast-paced business world, data is the lifeblood of organizations seeking to gain a competitive edge. Understanding the vast amounts of data generated within a company is crucial for making informed decisions. UiPath Communication Mining is a powerful tool that enables businesses to analyze and extract valuable insights from their communication data, such as emails, chat messages, and more.

Implementing UiPath Communication Mining:

In this step-by-step guide, we will walk you through the process of implementing UiPath Communication Mining, helping you unlock its full potential and harness the power of your data.

As discussed in the previous article, UiPath Communication Mining is an innovative technology that leverages artificial intelligence and machine learning algorithms to analyze unstructured communication data, including text messages, emails, audio transcripts, and social media interactions.

Uipath Communication Mining Key Concepts:

Before delving into the implementation process, let’s explore some of the key concepts of UiPath Communication Mining and How It Works…

Projects: Confined workspaces for managing specific data sets, linked to sources and datasets. Users need appropriate permissions to access data in a project, facilitating collaboration and analysis within teams or business units.

Datasets: A dataset allows the labelling of one or more sources to build a model. Sources can be included in multiple datasets. The labels in a dataset form a taxonomy, which can vary based on the use case, such as analytics or automation. For example, a dataset could encompass sales conversations to monitor customer experiences.

Source: Data is organized into sources, corresponding to channels like email, surveys, or customer reviews. Multiple sources can be combined to build a model, promoting better organization. Raw collections of verbatims (freeform text communications) grouped together, including responses from surveys, emails, chat transcripts, and call records. They are incorporated into datasets to structure and interpret the verbatims.

Verbatims and Metadata: Each verbatim contains associated metadata with structured data points providing additional information, including a timestamp for when it was created.

Clusters: Groups of similar verbatims organized using unsupervised learning techniques, essential for subsequent stages of model training. The platform presents 30 clusters at a time on the Discover page, each containing 12 verbatims.

Labels: Structured summaries of intents or concepts expressed within verbatims. Verbatims can have multiple labels, not limited to exclusive classifications. Labels are applied during model training and returned as predictions. They have confidence scores indicating the model’s likelihood of the prediction. A threshold can be set to convert the prediction into a “Yes/No” answer.

Comment: Each piece of text communication within sources is represented as a comment with an ID, timestamp, text body, and additional fields specific to the type of data. For example, emails have fields like “from” and “to,” while customer reviews have the review author.

Model: The model is continuously updated as users label more data. When querying the model for predictions, a specific model version number needs to be specified for consistent results.

Setting Up UiPath Communication Mining Account:

To access Communications Mining on Automation Cloud, follow pre-requisites such as enabling the service, being an existing user, and obtaining an invite from the Admin.

Pre-requisites for accessing Communications Mining on Automation Cloud:

Enable Communications Mining as a service on Automation Cloud tenant with an enterprise license and available AI units.
Be an existing user on the Automation Cloud tenant; if not, request an Admin to add you.

Process to obtain an account:

An Admin on Communications Mining should create a new user profile for you through the ‘Manage Access’ page in the Admin console within the platform.

Tenant-based access and permissions:

Access and permissions are specific to each tenant, not the entire organization. Ensure access is granted to the tenant where Communications Mining is enabled.

Note: The Admin cannot add you to Communications Mining until you accept your invite to the UiPath Automation Cloud.

Understand Permission in UiPath Communication Mining Account:

Default user permissions for new users in a project:

‘View labels’ and ‘View sources’ permissions are granted by default.
These permissions provide access to non-sensitive datasets, verbatims, and associated labels.

Limitation of default permissions:

Default permissions do not allow applying or removing labels within a dataset.
Additional permissions, like ‘Modify users,’ are required, which can be granted by another user with such authority in the project.

Admin access for Communications Mining via Automation Cloud:

Admins on the cloud tenant automatically receive Admin access on Communications Mining.
Admin privileges include ‘Sources permissions,’ ‘Datasets permissions,’ ‘Streams permissions,’ ‘Users permissions,’ ‘Buckets permissions,’ ‘Integrations permissions,’ and ‘Utility permissions.’

Navigating in UiPath Communication Mining Account:

Platform Navigation:

- The platform has two main navigation menus: the navigation bar and the admin console in the top right-hand corner.
- The navigation bar becomes visible after selecting a dataset from the Datasets page.
- The admin console provides access to ‘Sources,’ ‘Integrations,’ ‘Manage Access,’ and ‘My Account.’

Admin Console Elements:

- - ‘Sources’ lead to the Sources page for creating or updating project sources.
  - ‘Integrations’ allow creating and managing integrations.
  - ‘Manage Access’ navigates to the Projects page for creating new projects or adding users to existing projects.
  - ‘My Account’ leads to the My Accounts page for viewing account details and accessing API tokens.

Dataset Navigation:

- After selecting a dataset, users with ‘View Sources’ permissions can access various pages using the top navigation bar, such as Train, Discover, Explore, Validation, Reports, Models, Streams, and Settings.

Help Menu:

- The help menu, accessible via the ‘?’ symbol, provides access to various resources, including CM Support (knowledge base), CM API Docs (developer documentation), CM Training Academy (courses and training), UiPath community forum, UiPath Academy, YouTube tutorials, product release notes, product documentation and guides, and downloadable UiPath resources.

AI Units and Licensing Metering and Charging Logic:

AI Units and Licensing:

- AI Units are used to license AI products and are charged based on consumption when models bring value.
- Consumption cost is calculated as the sum of prediction cost and hardware cost.

Prediction Cost:

- Prediction cost is computed based on the input size and unit cost of the model.
- Different models have distinct unit costs and input size calculations.

Hardware Cost:

- The hardware cost for deploying ML Skills is determined by replicas and resource costs.
- The resource cost varies depending on the CPU, RAM, and GPU.

Consumption Example:

Given the scenario, AI Units are calculated for training and hosting the model and making predictions using models.

for Example, – *Communication Mining Charges AI Unit based on “Number of Messages” or ” No Of Text in Tickes” (Per message uploaded, modified, or predicted)

See Information on Source Page – AI Center – AI Units (uipath.com)

How to Implement UiPath Communication Mining :

Let’s dive into the step-by-step process of its implementation:

Step 1: Data Collection and Preparation

The first step in implementing Communication Mining is collecting the relevant communication data. This data can include emails, chat logs, audio recordings, social media messages, and more. Ensure that you have permission to access and analyze this data in accordance with privacy and data protection regulations.

Here we need to connect our communication channel of unstructured data which can be sourced from the Support Mailbox, Workflow or ITSM Tickets, Survey Responses, and Feedback Forms.

Data into communication mining can be imported via:

Live Integration using limited pre-built connectors such as Microsoft Exchange & Salesforce
Using API Integration
Or You can upload Historical Data exported from other systems via CSV or API

Step 2: Discover Data and Apply Labels

Once you have the required dataset the “UiPath Communication Mining Platform automatically starts the discovery process.

With the help of its unsupervised learning models, it clusters “Group of Communication” sharing similar themes. You can apply labels and entities that capture required data points.

This will act as the first stage of your Model training and provide a high-level view of “What’s in your Data …”

You should clearly define the objectives you want to achieve through the dataset.

Step 3: Train the Model

With the data loaded and objectives set, start utilizing the training modes to build out the training data for the model.

The platform enables any business user from the communication channel to become a Model Trainer without the requirement of data scientists or engineers.

With each training action, the platform continuously retrains, enhancing its comprehension of concepts and data points, leading to real-time updates in predictions.

By labelling a small, representative sample of training data, the platform gains the ability to automatically interpret and make predictions across the entire dataset at scale.

Step 4: Validate the Prediction

Structured label and entity predictions, each with their own confidence scores will be provided as part of the prediction. But before using this prediction in Automation Process or Take Decision we must validate the prediction.

Again the Tools functionality provides full transparency when it comes to performance, validating your model automatically each time it retrains.

With the help of “Model Rating” you will be able to make improvements as suggested as the next action item.

Step 5: Analyze, Monitor and Refine & Automate

The platform aggregates all of the predictions for labels and entities with key metadata to provide a wealth of query-able data, providing visibility into previously hidden processes and channels. Alongside analytics, we can deploy trained models to enable production automation.

Now Interpret the insights gained from Communication Mining and apply them to your business strategies and processes. Use the information to make data-driven decisions that align with your objectives.

UiPath robots and downstream systems can utilise the structured data created by UiPath Communications Mining to extend automation into service and conversation-based processes

As Communication Mining is an iterative process. Continuously monitor the results and refine your approach based on feedback and new data.

This ongoing refinement will ensure the tool’s effectiveness and relevance to your business needs.

Summary: The model retrain process in Communications Mining involves the creation of new model versions when labels or entity reviews are applied. The predictions are recomputed across the dataset, and the process can take time depending on training, dataset size, and the number of labels. Users can track the status of their model through helpful status indicators.

Model Retraining: Applying labels or reviewing entities leads to the creation of new model versions.

Recomputation of Predictions: The model recomputes predictions across the dataset during retraining.

Time Required: The retraining process can take time, influenced by training progress, dataset size, and label count.

Status Feature: Communications Mining provides a status feature to help users stay updated on their model’s progress.

Up-to-Date Indication: The status icons at the top of the page indicate the current status of the dataset.

Build Your Automation Using Communication Mining Prediction :

So far we have covered the various steps required for using the Communication mining platform. Once you are happy with the Model training you can follow the below steps to utilize the model in your automation use case :

Pinning the Model: The model needs to be pinned by toggling the “Save” option on the Models page.

Stream Configuration: Specify the stream name, dataset name, model version, and labels of interest, along with their corresponding thresholds.

Fetch-and-Advance Loop: Use the stream to fetch comments from the platform, acknowledging previous batches with an advance request.

Process Results: The response includes comments, predicted labels, and entities, which need to be parsed accordingly.

Exception Handling: The exception endpoint allows tagging incorrectly predicted items as exceptions for model improvement.

NOTE: You can also use Get Predictions for a pinned model using Prediction API Route.

References :

If you wish to explore more – read at provider’s website.

Introduction to Communications Mining here
How to navigate the platform here
Key concepts and terminology here
Quick training how-to’s here
FAQs, Tips & Tricks here

FAQs (Frequently Asked Questions) :

What is the Implementation Timeframe for UiPath Communication Mining?

The implementation timeframe depends on the size of your communication data and the complexity of your objectives. On average, the process can take a few weeks to a few months, considering data collection, preparation, and analysis.

Can UiPath Communication Mining Handle Multiple Languages?

Yes, UiPath Communication Mining supports multiple languages through its advanced NLP capabilities. It can analyze communication data in various languages, providing a global perspective on your business operations.

Currently, It supports all major European languages, but additional language support is coming soon.

Is Data Privacy a Concern with Communication Mining?

Data privacy is a top priority when implementing Communication Mining. Ensure that you comply with all relevant data protection regulations and obtain necessary consent from individuals whose data is being analyzed.

Can Communication Mining be Integrated with Existing Business Intelligence Tools?

Yes, UiPath Communication Mining can be integrated with your existing business intelligence tools, allowing seamless collaboration and data sharing across different departments.

Is UiPath Communication Mining Scalable?

Yes, UiPath Communication Mining is designed to be scalable, allowing you to analyze large volumes of communication data as your business grows.

UiPath Communication Mining: Extracting Insights from Conversations

Satish Prasad — Sun, 02 Jul 2023 12:39:43 +0000

UiPath Communication Mining: Introduction

In today’s data-driven world, organizations are constantly looking for ways to extract meaningful insights from their vast amount of communication data. UiPath Communication Mining is a cutting-edge solution that leverages the power of artificial intelligence to analyze and understand conversations across various channels.

By employing advanced natural language processing techniques, Communication Mining enables businesses to uncover valuable information hidden within their communication streams, leading to improved decision-making, enhanced customer experience, and optimized operations.

Let’s learn how UiPath Communication Mining helps businesses extract valuable insights from conversations. Explore the benefits, use cases, and implementation of this powerful tool for enhancing communication analysis and decision-making processes.

Throughout the #Blog Series, I will dive deep into the core concepts of communication mining and its integration with UiPath’s automation capabilities.

In 3 Parts, I will present practical examples, case studies, and real-world use cases to illustrate the potential of UiPath communication mining.

Additionally, I will discuss best practices and strategies for implementing communication mining in different business contexts effectively.

Are you ready to embark on this exciting journey into the realm of UiPath Communication Mining?

UiPath Communication Mining: Extracting Insights from Conversations

Implementing UiPath Communication Mining: Step-by-Step Guide

ServiceNow and UiPath Communication Mining Example: Enhancing ITSM Efficiency

Facts and Summary:

Business automation driven by AI, specifically natural language processing (NLP), is accelerating.
OpenAI’s ChatGPT chatbot has demonstrated advanced language understanding capabilities.
Large language models (LLMs) understand human language but may lack industry-specific knowledge.
Building a tailored AI solution for businesses remains difficult and expensive.
UiPath acquired Re:infer an NLP company, to enhance its AI capabilities.
UiPath Communications Mining is a new capability integrated into the UiPath Business Automation Platform.
Communications Mining uses AI models to extract actionable data from various business communication channels.
It helps businesses cut costs, improve service, and automate transactional customer requests.
UiPath Communications Mining offers three core functionalities: mining, monitoring, and automating communications.
It has been deployed successfully in major banks, insurance companies, and service providers.
Hiscox, a specialist insurer, reduced process lead time by 300% using Communications Mining.
The platform supports major European languages with additional language support coming soon.
On-premise deployment is available through UiPath Automation Suite.
The product is continuously being enhanced to extract intents and recognize relationships between data points.

UiPath Communication Mining: Unveiling the Power of Conversations

Conversations hold a wealth of information that can shape the future of businesses. Whether it’s customer interactions, internal team collaborations, or external communication with partners and suppliers, every conversation contains valuable insights waiting to be discovered. UiPath Communication Mining acts as a digital assistant, tirelessly sifting through these conversations to identify patterns, trends, sentiments, and key information that can drive business growth.

UiPath Communication Mining: How does Communications Mining work?

UiPath Communications Mining is an AI and ML-powered solution that extracts crucial information from business communications, making it enterprise-ready. It offers three core functionalities: mining communications for insights, monitoring service channels in real-time, and automating

Harnesses AI and ML: UiPath Communications Mining utilizes advanced AI and ML technologies for extracting important data from business communications effectively and reliably.

Safe and Reliable: The system ensures safety and reliability for enterprises while extracting crucial information from masses of communications.

Rapid Model Training: Business users can quickly train the model to tailor its output according to the specific needs of the business, using a user-friendly training interface.

Quick Deployment: ML models can be fully trained and deployed for production in a matter of hours, without any coding or technical expertise required.

Extracting Insights: The system extracts intents, reasons for contact, sentiment, and emotion from inbound messages, providing businesses with valuable insights into hidden processes and channels.

Real-Time Monitoring: UiPath offers a custom-filtered, real-time view of service channels, allowing businesses to track trends, measure service quality, and respond to issues in an agile manner.

Automation Capability: UiPath Robots use the data from Communications Mining to automate service and conversation-based processes, including triaging emails, updating customer information, and case creation.

Implementing UiPath Communication Mining

Implementing UiPath Communication Mining involves the following steps:

Data Collection: Gather communication data from various sources, such as emails, chat logs, voice recordings, and social media platforms.
Data Preprocessing: Clean and prepare the data by removing noise, formatting inconsistencies, and irrelevant information.
Natural Language Processing: Utilize advanced NLP techniques to extract valuable insights from the processed data. This includes sentiment analysis, topic modelling, entity recognition, and intent detection.
Insights Generation: Analyze the processed data to generate actionable insights, visualizations, and reports.
Integration and Automation: Integrate insights into existing business systems and processes, enabling real-time decision-making and automation.

Benefits of UiPath Communication Mining:

Actionable Insights: UiPath Communication Mining enables businesses to analyze conversations and gain valuable insights that can drive strategic decisions, enhance products, and improve customer experiences.

Enhanced Customer Experience: By understanding customer sentiments, preferences, and pain points, organizations can tailor their offerings and support services to effectively meet customer expectations.

Operational Optimization: Communication Mining identifies inefficiencies, bottlenecks, and areas of improvement in internal communications, helping organizations optimize operational processes and streamline workflows.

Regulatory Compliance: UiPath Communication Mining aids organizations in ensuring regulatory compliance by monitoring and analyzing communication data for potential violations.

Example Use Cases of UiPath Communication Mining:

Sales and Customer Service: UiPath Communication Mining provides insights into customer preferences, needs, and pain points, enabling sales and customer service teams to identify upselling opportunities, improve retention strategies, and enhance overall customer satisfaction.

Market Research and Competitive Analysis: Communication Mining helps businesses stay ahead by analyzing conversations related to market trends, customer feedback, and competitor insights, enabling data-driven decisions and real-time strategy adjustments.

Compliance and Risk Management: Organizations in regulated industries can use Communication Mining to monitor communication channels, identify compliance violations, and mitigate security risks, ensuring adherence to industry regulations.

Employee Engagement and Collaboration: Internally, Communication Mining enhances employee engagement and collaboration by identifying knowledge gaps, communication bottlenecks, and opportunities for improving team productivity.

The below use cases demonstrate the versatility of “UiPath Communication Mining” across various industries, helping organizations leverage insights from email, chat, and support tickets to improve efficiency, customer satisfaction, and decision-making processes.

Industry	Use Case	Description
Retail	Customer Sentiment Analysis	Analyze customer emails and chat interactions to gauge sentiment, identify issues, and improve satisfaction.
	Order Status Automation	Automate email communication for order status updates, reducing response time and enhancing customer experience.
Healthcare	Patient Support Ticket Analysis	Extract relevant information from support tickets to prioritize patient needs and provide timely assistance.
	Health Record Automation	Automate the extraction and processing of health-related data from emails for efficient record-keeping.
Banking & Finance	Fraud Detection	Analyze email patterns and chat interactions to detect potential fraudulent activities and prevent losses.
	Customer Query Resolution	Automate responses to frequently asked customer queries through email and chat, improving service efficiency.
Travel & Hospitality	Booking Confirmation Automation	Automate email parsing to confirm bookings, reducing manual work and enhancing booking accuracy.
	Real-time Chat Support	Utilize chat insights to provide personalized recommendations, enhancing customer engagement and loyalty.
IT & Technology	Ticket Categorization and Assignment	Use communication data to automatically categorize and assign IT tickets, streamlining the support process.
	Email Notification Automation	Automate email notifications for system alerts and updates, improving IT team responsiveness.
Education	Student Support Analytics	Analyze chat interactions and emails to understand common issues and improve student support services.
	Enrollment Process Automation	Automate email-based enrollment processes, reducing administrative workload and enhancing accuracy.
Manufacturing	Supply Chain Coordination	Utilize chat insights and email data to facilitate better coordination within the supply chain.
	Technical Support Ticket Analysis	Extract key information from support tickets to expedite technical issue resolution and enhance productivity.

Frequently Asked Questions (FAQs)

What is UiPath Communication Mining?

UiPath Communication Mining is an AI-powered solution that analyzes and extracts insights from conversations across various communication channels.

How does UiPath Communication Mining benefit businesses?

UiPath Communication Mining benefits businesses by providing actionable insights, enhancing customer experience, optimizing operations, and ensuring regulatory compliance.

Which industries can benefit from UiPath Communication Mining?

UiPath Communication Mining can benefit industries such as sales, customer service, market research, compliance, risk management, and internal collaboration.

What types of data can UiPath Communication Mining analyze?

UiPath Communication Mining can analyze various forms of communication data, including emails, chat logs, voice recordings, and social media interactions.

Is UiPath Communication Mining customizable for specific business needs?

Yes, UiPath Communication Mining can be customized to cater to specific business requirements, allowing organizations to focus on the most relevant insights and metrics.

How does UiPath ensure data privacy and security?

UiPath follows stringent data privacy and security measures to protect sensitive communication data. The platform adheres to industry-standard encryption protocols and ensures compliance with relevant data protection regulations.

Conclusion

UiPath Communication Mining offers businesses an unparalleled opportunity to extract valuable insights from their communication streams.

By leveraging advanced NLP techniques, organizations can uncover hidden patterns, sentiments, and trends that can drive strategic decision-making, enhance customer experience, and optimize operational processes.

With UiPath Communication Mining, businesses can harness the power of conversations and transform raw communication data into actionable intelligence.

UiPath Coded Automation: Revolutionizing the Future of Robotic Process Automation (RPA)

Satish Prasad — Fri, 30 Jun 2023 14:09:58 +0000

Low-code and no-code approaches have made automation accessible, but some technical users find them limiting. UiPath Studio plans to release coded automation in their upcoming version, which will provide more flexibility and collaboration opportunities for developers and simplify migration from legacy systems.

Introduction to UiPath Coded Automation

In the realm of software development, the advent of low-code and no-code solutions has revolutionized the way automation is built. These innovative approaches have empowered a new breed of developers, enabling even those with limited technical expertise to create powerful solutions for testing and business processes, also known as Robotic Process Automation (RPA).

However, for more technically inclined testers and RPA developers, the limitations of low-code automation often pose challenges regarding flexibility, comprehension, and maintainability.

UiPath Studio is proud to present the public preview of Coded Automation to address the needs of developers who prefer to work with code.

UiPath Studio introduces Coded Automation, a feature in public preview, to cater to developers with solid coding backgrounds.

Coded Automation provides flexibility and customization, empowering technical practitioners to create sophisticated automation.

It streamlines maintenance and collaboration through integration with version control systems and IDEs and unlocks innovation by leveraging external integrations and cutting-edge technologies.

UiPath Studio introduces Coded Automations, a feature in public preview, targeting developers with coding backgrounds.

Coded Automations offer enhanced flexibility, allowing developers to tailor automations to their specific requirements.

Coded Automations empower technical practitioners, such as skilled testers and seasoned RPA developers, to create sophisticated automations using their coding knowledge.

Coded Automations simplify maintenance and collaboration through integration with version control systems, IDEs, and collaboration tools.

Coded Automations unlock innovation by leveraging external integrations, cutting-edge technologies, and emerging trends.

How to Get Started with Coded Automation

The 23.6 Community release introduces an exciting feature called Coded Automation, which is now available as a Public Preview. This article will guide you through the process of getting started with Coded Automation and explore the various file types you can create.

Prerequisites

Install UiPath Studio 2023.6.
Install the versions of the activity packages, also known as services, supported in coded automation:
- System.Activities 23.6-preview
- UiAutomation.Activities 23.6-preview
- Testing.Activities 23.6-preview

Creating a New Project

To begin, you need to create a new Modern or Portable project. Both Visual Basic (VB) and C# are supported as workflow expression languages, providing you with flexibility in your development process.

Introducing the File Types

Once you have created your project, you can proceed to create three new types of files within the Coded Automation framework. Each file type serves a specific purpose and offers unique functionalities.

: How to Get Started with Coded Automation

1. Coded Workflows

Coded Workflows are not only equivalent to Sequences but also serve as entry points for your automation projects. For instance, the Main.xaml file in a new empty process project can be considered a Coded Workflow. It’s important to note that Coded Workflows can be set as the Main entry point as well. To invoke a Coded Workflow, you can use the Invoke Workflow File activity just like any other XAML file.

2. Coded Test Cases

Coded Test Cases, as the name suggests, are equivalent to Test Cases. They allow you to define and execute tests for your automation processes. With Coded Test Cases, you can ensure the reliability and accuracy of your automated workflows.

3. Code Source Files

Code Source Files represent files that contain source code. They serve a purpose similar to non-entry points XAML files, such as utility files or XAMLs that implement a part of the business logic. The code within source code files can be directly called in expressions inside XAML files, enabling seamless integration and flexibility. Additionally, you can use source code files to define more specific types of variables or arguments.

Working with Coded automations

Debugging: Debug-coded automation using breakpoints, debugging panels, and profile execution. Troubleshoot execution flow with debug running mode.

Running: Run coded automation in Studio’s Design tab by clicking “Run” for the whole project or “Run File” for the current file.

Validation: Use Workflow Analyzer to check for inconsistencies and view errors, warnings, and messages for both XAML (low-code automation) and CS files (coded automation).

Publishing: Publish a Studio project to Orchestrator, custom NuGet feed, or locally. Exclude test cases by selecting “Ignore from Publish” for specific files in the Project panel.

Supported Runtimes: Deploy coded automation on Robots supporting Windows framework versions 2022.4 and higher, and any supported version of Orchestrator.

Whats features do we have in Studio Code editor?

The Studio code editor interface facilitates efficient automation of project management, navigation, editing, and versioning. Key components include:

Code Editor: Displaying the current automation project, enabling changes, and highlighting errors. It functions like a standard code editor tool.

Context Menu: Accessible by right-clicking a line in coded automation, offering actions with keyboard shortcuts. Examples include Quick Actions, Rename, Go to Definition, Toggle Breakpoint, Find All References, Format Document, Comment, Uncomment, Cut, Copy, Paste, and Delete.

Tabs: Files open with tabs in the editor’s header for easy navigation and management.

Breadcrumbs: Available in the code editor header to navigate classes and methods within a file.

Versioning: Coded Automation projects can be versioned using semantic (Major.Minor.Patch[-Suffix]) or legacy (M.m.bbbb.rrrrr) versioning schemes. Projects can be versioned when published to Orchestrator or using source control systems like GIT, TFS, or SVN.

Creating your first UiPath-coded workflow

            using UiPath.CodedWorkflows;

namespace CodedAndLowCodeWorkflow
{
    public class _00_FirstCodedWorkflow : CodedWorkflow
    {
        [Workflow]
        public void Execute()
        {
            Log("Hello World from Coded Workflows!");
        }
    }
}

The code starts with the using directive using UiPath.CodedWorkflows;, which imports the necessary classes and functionalities from the UiPath.CodedWorkflows namespace.
The code defines a namespace CodedAndLowCodeWorkflow.
Within the namespace, a public class named _00_FirstCodedWorkflow is declared. This class inherits from CodedWorkflow, indicating that it is intended for coded workflows in UiPath.
Inside the _00_FirstCodedWorkflow class, there is a single method named Execute(), which is decorated with the [Workflow] attribute. This attribute is specific to UiPath coded workflows and marks the method as the entry point for the workflow.

Let’s Deep Dive – Create More Complex Examples slowly!

            using System.Windows.Forms;
using UiPath.CodedWorkflows;
using UiPath.Core.Activities;

namespace CodedAndLowCodeWorkflow
{
    public class _01_CodedWorkflowExample : CodedWorkflow
    {
        [Workflow]
        public void Execute()
        {
            // 1. Open Application 
            var ACMESite = uiAutomation.Open("LogIn");

            // 2. Get Assets from Orch.
            Log("Reading UserName & Password from UiPath Orch Assets...");
            var credential = system.GetCredential("ACME_Credential", null, out var password, CacheStrategyEnum.None, 30000);

            // 3. Type Into UserName
            ACMESite.TypeInto("Email", credential);

            // 4. Type Into password
            var actualPassword = new System.Net.NetworkCredential(string.Empty, password).Password;
            ACMESite.TypeInto("Password", actualPassword);

            // 5. Click the Submit button to perform the Login
            ACMESite.Click("Login");

            //6. Check if Screen Changed
            var dashboard = uiAutomation.Attach("Dashboard");
            var Description = dashboard.GetText("User options");

            // 6. Display Message 
            Log("Dashboard Appeared..");

        }
    }
}

Creating hybrid automation – Combining Coded and Low-code Workflows

Explore multiple ways of building hybrid automation by combining low-code automation with coded automation. The following scenarios will be covered:

Invoking a coded source file from a low-code workflow

Leveraging coded automation within a low-code workflow with arguments

Invoking one coded automation from another

Integrating a low-code workflow into coded automation

Invoke Coded Workflow File in LowCode Workflow file

You can invoke coded workflow in .xaml file the same way as you do invoke other xaml files.

PS – Arguments will not come automatically you might need to add them manually.

Invoke Coded Automation workflow inside Another Coded Automation workflow

The RunWorkflow method is called to execute another workflow, specified in “03_CodedWorkflowWithArguments.cs.” This method takes two arguments:

The filename of the workflow to run (as a string).
A dictionary containing input arguments to pass to the executed workflow.

The input arguments are defined in the dictionary:

“assetName”: This argument is set to “MyAsset” and represents the name of the asset to be used in the executed workflow.
“assetValue”: This argument is set to “hello world” and represents the value to be assigned to the “MyAsset” asset.

The RunWorkflow the method returns a dictionary containing the result of the executed workflow.

            using System.Collections.Generic;
using UiPath.CodedWorkflows;


namespace CodedAndLowCodeWorkflow
{
    public class _04_CodedWorkFlowUsingCodedWorkFlow : CodedWorkflow
    {
        [Workflow]
        public void Execute()
        {
            // Requirements:
            // - Create an asset of type Text called MyAsset in the current folder.

            var result = RunWorkflow("03_CodedWorkflowWithArguments.cs", new Dictionary(){
                        {"assetName", "MyAsset"},
                        {"assetValue", "hello world"}
                    });

            if ((bool)result["assetValueWasChanged"])
            {
                Log("Reset asset MyAsset, but it had a different value, previous value was " + result["assetValue"]);
            }
            else
            {
                Log("No reset was required on asset MyAsset, which had the expected value.");
            }
        }
    }
}

Invoke .xaml(LowCode Automation Workflow) in .cs(Coded Automation Workflow)

The RunWorkflow method is called to execute the low-code workflow named “Login.xaml”. This method takes two arguments:

The filename of the low-code workflow to run (as a string). In this case, it is “Login.xaml”.
A dictionary containing input arguments to pass to the executed low-code workflow.

The input argument is defined in the dictionary:

“in_AssetName”: This argument is set to “ACME_Credential” and represents the name of an asset to be used in the invoked low-code workflow.

The RunWorkflow method returns a variable result that can be used to capture the output of the low-code workflow execution. However, in this code snippet, the result variable is not utilized further.

            using System.Collections.Generic;
using UiPath.CodedWorkflows;

namespace CodedAndLowCodeWorkflow
{
    public class _05_LowCodeWorkFlowInCodedWorkflow : CodedWorkflow
    {
        [Workflow]
        public void Execute()
        {
         
            Log("Lets Invoke LowCode Workflow here..!");
            var result = RunWorkflow("Login.xaml", new Dictionary()
              {
                {"in_AssetName", "ACME_Credential"},
              });
        }
    }
}

Why Choose Coded Automations Over Low-Code Solutions?

Coded automation offers higher productivity and flexibility, allowing developers to handle complex automation tasks with ease.

Coded automation has an edge in collaboration, enabling efficient code reviews, bug fixing, and simultaneous work on different parts of the automation project.

Coded automation provides structured and organized code, enhancing readability and maintainability compared to convoluted logic in low-code solutions.

Coded automation allows fine-tuning for optimal performance, including algorithm optimization and efficient resource utilization.

Transitioning to coded automation is easier for developers with coding experience, leveraging existing knowledge and skills.

Developers can unlock a new level of productivity, complexity management, collaboration, reviewability, readability, and performance with coded automation.

Coded automation empowers developers to create powerful, efficient, and customizable automation.

GitHub REPO

Get My Example Code

References :

UiPath/codedautomations-samples: Samples for UiPath Coded Automations (github.com)

What makes UiPath Coded Automation stand out from traditional RPA?

UiPath Coded Automation stands out from traditional RPA due to its ability to utilize coding languages like C#, VB.NET, and Python(planned for future release) providing unmatched flexibility and customization. It empowers developers with advanced features, seamless integration, and improved reusability, setting it apart in the RPA landscape.

Can beginners with little coding experience benefit from UiPath Coded Automation?

Yes, beginners can benefit from UiPath Coded Automation. While having some coding experience can be advantageous, UiPath’s extensive documentation, tutorials, and community support make it accessible to developers with varying levels of expertise.

Does UiPath Coded Automation support integration with third-party applications?

Yes, UiPath Coded Automation supports seamless integration with third-party applications. Its compatibility with coding languages allows developers to connect with various APIs and external systems effortlessly.

Can UiPath Coded Automation be combined with exiting Low Code Workflow automation techniques?

Absolutely! UiPath Coded Automation can be seamlessly combined with visual automation techniques offered by UiPath Studio. This hybrid approach further extends the potential of automation, making it more versatile and adaptive.

Can I write UiPath Coded Automation in Other Third-party Tools like VSCode ?

As of NOW NO. But in the future Uiapth plans to provide extensions and library to be utilised in different leading IDE such as Visual Studio.

Conclusion:

Coded Automation in UiPath Studio represents a groundbreaking advancement in the field of test and business process automation. By catering to the needs of technical practitioners and offering a code-centric approach, UiPath empowers developers to unleash their creativity and build highly customized and efficient automation. With the ability to seamlessly integrate with existing coding practices and the freedom to innovate, Coded Automations are set to redefine the way automation solutions are created and maintained.

Experience the power of Coded automation and revolutionize your automation journey with UiPath Studio today!

The Complete Guide to UiPath CI CD with Azure DevOps

Satish Prasad — Sat, 23 Jul 2022 19:22:16 +0000

The Complete Guide to UiPath CI CD with Azure DevOps

Last updated: 08/12/2023 4:37 am

Satish Prasad

37 Min Read

Step by step working example to setup CI/CD pipeline for UiPath using Github and Azure DevOps

DevOps is a mindset, a way of working for an organization so that new features reach to the users as quickly as possible and smoothly too.

There have been discussions regarding how continuous integration and deployment concepts can be applied to the RPA development cycle.

Topics covered in this article –

What is CI/CD in the context of RPA (Focus on UiPath)
Key Concepts in Azure Pipelines
Build the first pipeline to package UiPath project
Different ways of UiPath CI/CD pipeline creation using Extension or Powershell
Required Service Connection configuration
Configure self-hosted agent
Complete working example.

Let’s get started!

Are you looking for DevOps integration using Jenkins

How to Create a Pipeline for UiPath Using Jenkins, Step by step guide, Fully working example.

See here

What is a CI/CD pipeline?

A CI/CD pipeline workflow usually consists of the following discrete steps:

Phase 1: Commit – When developers complete a change, they commit the change to the repository.
Phase 2: Build- Source code from the repository is integrated into a build.
Phase 3: Automate tests- Automated tests are run against the build. Test automation is an essential element of any CI/CD pipeline.
Phase 4: Deploy- The built version is delivered to production.

Continuous Integration (CI)

Continuous integration (CI) is the practice used by development teams to simplify the testing and building of code. CI helps to catch bugs or problems early in the development cycle, which makes them easier and faster to fix. Automated tests and builds are run as part of the CI process.

The process can run on a set schedule, whenever code is pushed, or both. Items known as artefacts are produced from CI systems. They’re used by the continuous delivery release pipelines to drive automatic deployments.

Continuous Delivery (CD)

Continuous delivery (CD) is a process by which code is built, tested, and deployed to one or more test and production stages. Deploying and testing in multiple stages helps drive quality.

Continuous integration systems produce deployable artifacts which are used in CD to release new versions and fixes to existing systems. This process ensures that errors are caught often and early.

CI/CD in Context of RPA Tool UiPath

Although many organizations have started implementing a Digital Workforce into their business operations – only a few are fully in control of their RPA bots.

Very few COE has already implemented the CI/CD (Continuous Integration, Continuous Delivery) pipeline that allows you to automate your robot delivery process.

A typical pipeline for UiPath can be –

The pipeline will initiate the build from your version control system.
The pipeline will also perform the creation of various assets, config replacement, creating initial folders etc.
The pipelines will publish the package to Orchestrator.
It can further include workflow analyser to code review, approval workflow, integration with ITIL tools such as(service now) for change board approvals.
It can be further improved to Run test cases from Orchestrator.

Why should You use Azure Pipelines?

An azure pipeline is free if you want to implement it for public projects. (You can run 30 Hours free pipeline even if you have a private project)
Its helps to implement CI and CD pipelines to ensure best code quality and work with any language or platform you wish to use.
You can use Azure Pipelines with most application types, such as Java, JavaScript, Node.js, Python, .NET, C++, Go, PHP
Build can be used on Windows, Linux, or Mac machines. (However, for UiPath build we need only Window-based Agents.)
Azure Pipelines deploy your code to multiple targets. Targets include container registries, virtual machines, Azure services, or any on-premises or cloud target. You can configure it as per your organization need.

Key Concepts in Azure Pipelines

Before I talk about building an azure pipeline for Build, Test and Deploy. You need to understand the basic building blocks of the Pipeline. Such as-

Agents
Conditions
Expressions
Jobs
Stages
Tasks

Let’s quickly discuss the key concepts used in the azure pipeline.

An Agent is computing infrastructure with installed agent software that runs one job at a time. You can treat the agent as executor which actually performs the jobs as instructed by the pipeline stages. Your agent must have the capability to perform a certain action. In our example, UiPath must be installed on an agent to perform build/test.
A trigger tells a Pipeline to run. In the above example, It’s the setup that tells when the pipeline needs to be run. You can configure a pipeline to run when you publish your UiPath Projects to Git or Repo you are using. This can be also configured to run at scheduled times or upon completion of another build.
A Stage is a logical grouping of Jobs in the pipeline and each stage can have one or more jobs that run on Further your job can contain one or more step(task/script)
A Step can be a task or script and is the smallest building block of a pipeline. For example, you are creating a pipeline which consists of build and test steps both. Your step can be either a ‘task’ [Custom Code] Or Script written in PowerShell or Shell Script. For example, there is a task, that task has got five different stages, and each stage has got some steps. All the steps in phase one have to be completed, to mark the latter stage to be complete.

Using Azure DevOps

As Discussed above, Azure Pipelines supports continuous integration (CI) and continuous delivery (CD) to constantly and consistently test and build your code and ship it to any target.

You accomplish this by defining a pipeline.
You define pipelines using the YAML syntax or through the user interface (Classic).
You can also use the YAML templates provided. Make sure to update variables as per your need.

How to implement a CICD pipeline using Azure DevOps Pipelines?

Alright ! So far we have discussed details about the Azure DevOps platform, Let’s take a basic example of creating a pipeline to create a NuGet package.

Once we understand the work we will move to the next example of the full-fledge project working.

Let’s jump in!

Step 1: Prerequisites - Azure DevOps

Before you use continuous integration and continuous delivery practices for your applications, you must have your project source code in a version control system. Azure pipelines can integrate with GitHub, TFVC, Bitbucket Cloud, Azure Repos Git and SVN…
An Azure DevOps organization. If you don’t have one, you can create one for free. (An Azure DevOps organization is different from your GitHub organization. Give them the same name if you want alignment between them.)
If your team already has one, then make sure you’re an administrator of the Azure DevOps project that you want to use.
Use the following URL to sign in to your organization at any time: https://dev.azure.com/{yourorganization}
You need to have Windows-Based Build Agent as now its only Support Windows Agent. You can create Pool of Agents or Simply use your Laptop as Agent for learning purpose.

Step 2 : How to Set Up Azure DevOps & UiPath Extension for Azure DevOps [One Time Setup]

Implement continuous integration and continuous delivery (CI/CD) for the app and platform of your choice require set-up to be done before writing your first pipeline.

Sign up for an Azure DevOps organization – if you have existing Microsoft account, all you need to do is click on “Start free” to get your account created.
It will ask you to login using Microsoft account, you can use your personal account as well. Alternatively, you can choose “Start Free with GitHub”.
Default Organization will be created with your email as yourorganization. Which can be accessed later using https://dev.azure.com/{yourorganization}
You should be able to create as many as Azure DevOps organization you need by simply giving the Organization Name and hosting location.
The next step is creating a project by specifying the Project Name, Description, Version Control System (Such as Git) and the most important is Visibility.
If you use your project type as public projects, Azure Pipelines is free. If you use private projects, you can run up to 1,800 minutes (30 hours) of pipeline jobs for free every month.
Once Project is created you should be able to Create your first Pipeline using step by step Wizard.
You can connect your GitHub Account to integrate the projects you have published in a git repo.
You also need to provide Approve & Install Azure Pipelines so that required permissions are granted on the repo.

You can read more details here – [Signup for Azure Pipeline]

An AzureDevOps extension is also available that allows you to build and deploy UiPath automation processes, as well as run UiPath automated test cases. For using the extension you need to install it on Azure by getting it from the visual studio marketplace.(Check uipath.vsts-uipath-package)

Next, we need to install the UiPath Integration extension to your Azure DevOps organization. The UiPath Integration for Azure DevOps comes with a pack of pre-created tasks that allow you to build and deploy UiPath automation processes

It provides following built-in capability-

The UiPathPack task enables you to package an existing UiPath project into a NuGet package.
The UiPathDeploy task lets you deploy a UiPath NuGet package onto UiPath Orchestrator.
The UiPathTest task runs an existing test set on Orchestrator, or packages, deploys and runs test cases as a transient test set in one go, by specifying the path to a UiPath test project.
The UiPathAssets enables you to deploy, update or delete assets on an Orchestrator instance. In order to deploy assets, you must describe them in a CSV file encoded in UTF-8.
The UiPathInstallPlatform has a prepackaged tool that all the other UiPath tasks are using. The task is optional, but it should be used as it reduces the time to execute the other tasks. The task is required when build agents do not have access to https://www.myget.org/F/uipath-dev/. The task should be executed before all other tasks in the extension.
The UiPathRunJob task starts an already deployed process on an Orchestrator instance. The processes this task refers to are found in Automations->Processes on newer versions of Orchestrator and directly on the Processes tab on older versions of Orchestrator.

For the UiPathDeploy and UiPathTest tasks, you will also need to first create a Service Connection. (Enable API Access and set required).

You can go to Project Settings ->Service Connections -> UiPath Orchestrator Connection and add the service connection details. For best practices, it’s required to create multiple tenants such as (DEV, UAT, and PROD) and configure them separately.

Step 3: Create your first pipeline

At this point of post, I assume you already has setup the organization and projects in the Azure DevOps portal and now you are ready for creating your first pipeline.

Creating a pipeline is super easy if you understand the basic building blocks of Azure DevOps key concepts
All you need to do is add an “azure-pipelines.yml” file into your source code repository. if you are using the templates, or you can create it on Azure DevOps portal.
Let’s Create the first pipeline which will build the UiPath Project as. nupkg, we will look at the complete example later where I will build multiple stages to perform build test and deploy on orchestrator as well.

In the Azure DevOps project, click on “Pipelines” on the left menu of the page. Click on “New pipeline”. The following window will appear which will ask you to provide the details of your version control system.

You can select your GitHub repository; it will also ask you to provide access to manage code and other permission. You need to accept and install here. (If you are already connected you can select the project from the dropdown.)

Once access for source code has been granted, you need to specify whether you are going to use.

- Starter pipeline- Minimal setup
- Use Existing Azure Pipeline YAML file.

If you have not created “azure-pipelines.yml” in your source code, you should select the Starter pipeline and edit the required details on the editor.

Copy the below code into the window(by replacing template) and save the file. All we are doing here is using the “UiPathRobot.exe file on the agent to pack the UiPath Project as. nupkg.

            trigger:
 - master
stages:
 - stage: Build
   displayName: Create build artifact
   jobs:
     - job: BuildArtifact
       pool: MYLAPTOP
       steps:
         - script: C:\Users\Satish\AppData\Local\UiPath\app-20.10.0-beta0149\UiRobot.exe pack $(Build.SourcesDirectory)\project.json -o $(Build.ArtifactStagingDirectory)\ -v 1.0.$(Build.BuildNumber)
           displayName: "Build the artifact"
         - publish: $(Build.ArtifactStagingDirectory)
            artifact: drop

The key point to note here is –

We have used trigger as “master” that means any changes in the repo will trigger the pipeline. You can have multiple configurations.
In the next line, Stages were specified with jobs and steps and the only step that is mentioned here is Running “UiPathRobot.exe pack” command. the executable location will be different fro you and it depends on the installation. So update that accordingly.
We have used the agent pool as “MYLAPTOP” as I have created agent on my laptop instead of using any VM or Docker, you can follow the configuration step to create Agent [See details in Configuration Section]

So now you are ready to Run the first pipeline.

If you don’t love GUI Steps, You can also use Azure CLI (command-line interface), See more here about creating a pipeline from the CLI(create-first-pipeline-cli ).

Once you run it you will see that “UiPathAzureDevOps.1.0.20201004.6.nupkg” gets published at the work location.

Congratulations! You are done with your first pipeline to successfully build the .nupkg.

But we are not done yet!

In the real project world, there will be a series of stages which includes various steps in different environment and approval and settings done by your organisation.

You might need to perform an automated test as well after publishing in UAT or perform a smoke test to see if everything is good.
You might need to include approval workflow where few tasks get completed and then it requires stage gates approval from the production support team.
You might need change tickets workflow included in the pipeline to create change tickets with description and updating tickets once change gets completed.
You might need to configure different assets in a different environment and inside different tenants of the orchestrator.

And so on…all that I want to convey here is creating a pipeline for UiPath depends on the practices being followed in your organisation.

In the next section, I will talk about the different ways of creating a pipeline with the help of UiPath Extensions available for Azure DevOps and Via PowerShell Utility.

Azure DevOps CI/CD pipeline Templates for UiPath Projects and Library

Many developers have created a pipeline for UiPath and they have also published the template for UiPath Projects and library on GitHub. Which can be utilized to save your effort.

Here are few GitHub repo which can be used to build you pipelines from template.

You can clone the Templates files from the above repo to start it quickly.

UiPath Integration for Azure DevOps [Using UiPath PowerShell CLI]

In the above example, we have used the inline script to build the first pipeline. However, UiPath also provides support for PowerShell cmdlet for all the task you need to perform with UiPath Orchestrator.

This provides a better way in case you are not interested in using the extension and you can use PowerShell cmdlet inside your pipeline to perform the required task.

For example- You can get the UiPath AuthToken using the UiPath.Powershell and then use Add-UiPathPackage to publish the package on orchestrator.

Import-Module UiPath.Powershell
$token = Get-UiPathAuthToken -ClientId $(uatClientId) -UserKey $(uatUserKey) -AccountName $(uatAccountName) -CloudDeployment ‘Production’ -TenantName $(uatTenantName)
Add-UiPathPackage -PackageFile $(Pipeline.Workspace)\drop\project.1.0.$(Build.BuildNumber).nupkg -AuthToken $token

You can read more details of all the PowerShell Cmdlet here – [Orchestrator-PowerShell docs ]

How to Set up Self-Hosted Build Agents in Azure DevOps

To run a build or release pipeline, a container is used in Azure DevOps to run the tasks in the pipeline. The containers are known as build agents and they vary in use case depending on the type of application being deployed.

In our case, we are using the UiPath Project so we need agents which are capable of building projects and running the UiPath test cases.

For sake of simplicity, you can say that running a UiPath CI/CD Pipeline we need an agent (Build Machine) which have UiPath already installed and configured.

For trying our hands out, we have set up a build agent on Windows 10.

You need to follow the following steps for setting Up Build agents –

Personal Access Token (PAT)

Personal Access Token (PAT) is an authentication method used to authenticate your agent, so the first thing you need to do is navigate to the user profile and click Personal access tokens to access the PAT settings.

Download the Agent setup

Download Azure DevOps agent as per your system (32bit x86 or 64bit x64) configuration and Operating System.

Configure the Agents

Once Downloaded, it can be configured easily, Unpack the agent into the directory of your choice. Then run config.cmd. This will ask you a series of questions to configure the agent.

Personal Access Token (PAT) is an authentication method used to authenticate your agent, so the first thing you need to do is navigate to the user profile and click Personal access tokens to access the PAT settings.
You need to provide PAT Name, Scopes which provides access associated with the token. You can select the full access or Custom defined as per your need.
It will provide you secure string, make sure to copy that save it in secure location this will be used in the next step to connect your agent with the organization.
Once you have PAT, Next step is to define the Agent Pool, for creating the Agent Pool, Go to Organization Settings and Navigate to Pipeline, Under Pipelines, click Agent Pools to access the agent pool settings.
Click on Add Pool and fill required details such as “Win10Agents”. You should also check pipelines permissions so required access are granted.
So Far, we have created the agent pool with name “Win10Agents” but no agent is added into the pool, so navigate to New Agent Page and agent as per your system (32bit x86 or 64bit x64). Click the Download button to download the agent and configure it on your machine.
Make sure your machine has these prerequisites:

1. Windows 7, 8.1, or 10 (if using a client OS)
2. Windows 2008 R2 SP1 or higher (if using a server OS)
3. PowerShell 3.0or higher
4. .NET Framework6.2 or higher

Once Downloaded, it can be configured easily, Unpack the agent into the directory of your choice. Then run config.cmd. This will ask you a series of questions to configure the agent.

You will require to enter the “Organization URL” and “PAT” which is –
- When setup asks for your server URL, for Azure DevOps Services, answer https://dev.azure.com/{your-organization}.
- When setup asks for your authentication type, choose PAT. Then paste the PAT token you created into the command prompt window.
Once done, You can run the agent using the .run or Simply right click and run as administrator.
Under agent pools, you should be able to now see the Windows 10 build machine online.

Other Configuration Steps

To add an Orchestrator Service Connection (based on the tenant)

In the Azure DevOps project, click on Project Settings on the bottom left of the page.
Click on Service connections under Pipelines.
On the top right of the page, click New service connection.
Choose UiPath Orchestrator Connection as the type and click next.
For on-premise Orchestrators, you will be using Basic Authentication with a username and password. Specify the Orchestrator URL and tenant as well.
Name the connection (it is a good idea to include the environment and tenant in the name) then click save.

To add variables to store client id/user key etc.

In the Azure DevOps project, click on Project Settings on the bottom left of the page.
Click on Library under Pipelines.
On the top of the page, click + Variable Group. for this example, we have given group name as “UiPath”
You can now add all the variables you need inside the pipeline. (for our use case we need Orchastarator API Details so so that we can publish the package to the different environment )
For reusability, you should create different groups for different environment and projects. this will help to restrict the usages.

To change the list of approvers

In Azure DevOps project, select Environments under Pipelines.
Select the environment you wish to edit.
On the top right, click on the ⋮ icon to expand the menu.
Select Approvals and checks.
Either select the existing approval or create a new one if none exist.
Add or remove approvers as needed.

Complete Working Example for UiPath CI CD Pipeline With Azure DevOps Project

Example #1 - Using azure-pipelines.yml (Pipeline codes in Project )

Pipelines Details

As discussed, Pipelines are the top-level component of continuous integration, delivery, and deployment. In this working example, we are going to create a pipeline comprising-

Build Stage – This will create build artefacts using a self-hosted agent
Deploy Stage – This will publish the packaged.nupkg to the UAT orchestrator
Test Stage – This will run Test Cases with the published package in UAT.
Deploy to Prod Stage – Finally, the package will be deployed in PROD env.

This example will give you the required information to set up your pipeline. You can add the required stages accordingly.

Working Pipeline Yaml Code

            trigger:
  - master
variables:
  - group: DEV

stages:
  - stage: Build
    displayName: Build Package
    jobs: 
    - job: BuildPackage
      pool: LAPTOP-Q338O4FK
      steps:    
      - task: UiPathPack@2
        inputs:
          versionType: 'AutoVersion'
          projectJsonPath: '$(Build.SourcesDirectory)\project.json'
          outputType: 'Process'
          orchestratorConnection: 'UiPath DEV'
          outputPath: '$(Build.ArtifactStagingDirectory)'
      - publish: $(Build.ArtifactStagingDirectory)
        artifact: drop

  - stage: Assest
    displayName: Create Assests
    jobs:
      - job: CreateAssests
        pool: LAPTOP-Q338O4FK
        steps:
        - task: UiPathAssets@2
          inputs:
            orchestratorConnection: 'UiPath DEV'
            folderName: 'Shared'
            assetActionType: 'Deploy'
            csvFile: '$(Build.SourcesDirectory)\Data\Assests.csv'

  - stage: DeployDEV
    displayName: Deploy build artifact to DEV
    dependsOn: Build
    condition: succeeded()
    jobs:
      - deployment: deployDEV
        displayName: Deploy package to DEV Orchestrator
        pool: LAPTOP-Q338O4FK
        environment: DEV
        strategy:
         runOnce:
          deploy:
           steps:
            - task: UiPathDeploy@2
              inputs:
                orchestratorConnection: 'UiPath DEV'
                packagesPath: '$(Pipeline.Workspace)\drop\'
                folderName: 'Shared'
                
  - stage : CodeQuality
    displayName: Check Code Analysis
    dependsOn: Build
    condition: succeeded()
    jobs:
      - job : CodeQuality
        steps:
          - task: SonarCloudPrepare@1
            inputs:
              SonarCloud: 'Sonar'
              organization: 'rpabotsworld'
              scannerMode: 'CLI'
              configMode: 'manual'
              cliProjectKey: 'rpabotsworld_UiPathAzureDevOpsExample'
              cliProjectName: 'UiPathAzureDevOpsExample'
              cliSources: '.'
          - task: SonarCloudAnalyze@1
          - task: SonarCloudPublish@1
            inputs:
              pollingTimeoutSec: '300'


  - stage: TestDEV
    displayName:  Test After Publish
    dependsOn: DeployDEV
    condition: succeeded()
    jobs:
      - job: TestDEV
        pool: LAPTOP-Q338O4FK
        steps:
          - task: UiPathTest@2
            inputs:
              testTarget: 'TestSet'
              orchestratorConnection: 'UiPATH RPABOTSWORLD'
              testSet: 'UiPathAzureDevOps_Tests'
              folderName: 'Shared'
              testReportDestination: '$(Pipeline.Workspace)\drop\'

Stage Overview

You can see the details of individual stages by navigating to jobs run details page.

Example #2 - Using project-pipeline-template.yaml (Pipeline code as Library)

Library for Staged pipeline with an approval process for production releases

Now Playing 1/1

Mastering Azure DevOps CI/CD Pipeline for UiPath Development: Automate Your RPA Delivery

            # Staged pipeline with an approval process for production releases.
#
# Approvals are not defined here, but rather tied to the environment. 
#
# As is, this pipeline template will deploy to the DEV environment on commits to any developement* branch without approvals,
# will deploy to the UAT environment on commits to any release* branch without approvals,
# and will deploy to the Prod environment on commits to the master branch, after approval.

parameters:
# The full path to the folder containing the project.json file for this pipeline
- name: 'projectPath'
  default: '.'
  type: string
# The name of the folder to deploy the package to.
- name: 'folderName'
  default: 'Default'
  type: string
# Either AutoVersion to generate a build number or CurrentVersion to match the project.json version.
- name: 'versioningStrategy'
  default: 'CurrentVersion'
  type: string
# The environments to update the package version to the deployed version. 
# Not required for modern folders or if you do not want to update the package version.
- name: 'environments'
  default: ''
  type: string
# The name of the testSet to be executed 
- name: 'testSet'
  default: 'Azure.Devops_Tests'
  type: string
  
# This pipeline is broken into stages for the approval functionality. Stages are ran independantly, which means the pipeline can pause until the approval is received. 
stages:
# Build the nuget package.
- stage: Build
  jobs:
  - job: BuildJob
    pool:  Default # Update this if using dedicated build pool
    workspace:
      clean: all
    steps:
    - script: 'echo project path: ${{ parameters.projectPath }}, folder name: ${{ parameters.folderName }}, versioning strategy: ${{ parameters.versioningStrategy }}, environments: ${{ parameters.environments }}'
      displayName: Log parameters
    - task: UiPathInstallPlatform@2  # This installs required exes. Not necessary if using a dedicated build machine.
    - task: UiPathPack@2
      inputs:
        versionType: ${{ parameters.versioningStrategy }}
        projectJsonPath: '$(Build.SourcesDirectory)\${{ parameters.projectPath }}'
        orchestratorConnection: Orchestrator-Dev-Default  # Update this to a service connection for your Orchestrator.
        outputPath: '$(Build.ArtifactStagingDirectory)\Output'
    # Publish the nuget package for later stages.
    - publish: $(Build.ArtifactStagingDirectory)\Output
      artifact: drop

# Deploy to the Test environment on commits to any release* branch.
# Note that this stage has no environment defined, and won't have approvals.
# For Test environment approvals, update this to look like the Prod stage, but with using the Test environment.
- stage: DeployToTest
  condition: and(succeeded('Build'), startsWith(variables['Build.SourceBranchName'], 'development'))  # Only run if the packaging succeeded and we are on a development* branch.
  jobs:
  - job: DeployToTestJob
    pool: Default  # Update this if using dedicated build pool
    workspace:
      clean: all
    steps:
    - download: current
      artifact: drop
    - task: UiPathInstallPlatform@2  # This installs required exes. Not necessary if using a dedicated build machine.
    - task: UiPathDeploy@2
      inputs:
        orchestratorConnection: Orchestrator-Dev-Default
        packagesPath: '$(Pipeline.Workspace)\drop'
        folderName: ${{ parameters.folderName }}
        environments: ${{ parameters.environments }}

- stage: Assest
  displayName: Create Assests
  jobs:
    - job: CreateAssests
      pool: Default  # Update this if using dedicated build pool
      steps:
      - task: UiPathAssets@2
        inputs:
          orchestratorConnection: Orchestrator-Dev-Default
          folderName: ${{ parameters.folderName }}
          assetActionType: 'Deploy'
          csvFile: '$(Build.SourcesDirectory)\Data\Assests.CSV'

- stage: Testing
  displayName:  Test After Publish
  dependsOn: DeployToTest
  condition: succeeded()
  jobs:
    - job: TestDEV
      pool: Default
      steps:
        - task: UiPathTest@2
          inputs:
            testTarget: 'TestSet'
            orchestratorConnection: Orchestrator-Dev-Default
            testSet: ${{ parameters.testSet }}
            folderName: ${{ parameters.folderName }}
            testReportDestination: '$(Pipeline.Workspace)\drop\'


# Deploy to the Prod environment on commits to the master branch.
# Will require approvals as defined by the environment in Azure Devops.
- stage: DeployToProd
  condition: and(succeeded('Build'), eq(variables['Build.SourceBranchName'], 'master'))
  jobs:
  - deployment: DeployToProdJob
    pool:  # Update this if using dedicated build pool
      vmImage: 'windows-latest'
    workspace:
      clean: all
    environment: Production  # Update this to your Prod Enviornment in DevOps. This is where you configure the approval process.
    strategy:
      runOnce:
        deploy:
          steps:
          - download: current
            artifact: drop
          - task: UiPathInstallPlatform@2  # This installs required exes. Not necessary if using a dedicated build machine.
          - task: UiPathDeploy@2
            inputs:
              orchestratorConnection: Orchestrator-Dev-Default  # Update this to a service connection for your Prod Orchestrator.
              packagesPath: '$(Pipeline.Workspace)\drop'
              folderName: ${{ parameters.folderName }}
              environments: ${{ parameters.environments }}

Using the template repository in the Project folder

            # Copy this file to you project's root directory (containing the project.json)
# then update the variables and paths trigger as a appropriate.
#
# The resouces section points to the repository containing the pipeline templates. This is set to UiPath's repository.
# DO NOT use the UiPath template repository for anything other than testing. It may change without notice.
# Please fork/replicate this repository to host it yourself and make any required modifications.

variables:
  # The full path to the folder containing the project.json file for this pipeline
  projectPath: project.json
  # The name of the folder to deploy the package to.
  folderName: Shared
  # Either AutoVersion to generate a build number or CurrentVersion to match the project.json version.
  versioningStrategy: AutoVersion
  # The orchestrator tenant to deploy to. Not used, but may be useful if deploying to multiple tenants.
  tenant: DEV
  # The environments to update the package version to the deployed version. 
  # Not required for modern folders or if you do not want to update the package version.
  environments: ''

# Update this trigger to specify when the pipeline should run.
# Variables cannot be used here.
trigger:
  # Define what branches trigger the pipeline
  branches:
    include:
      - master    # Will push to Prod after approval
      - development # Will push to DEV without approval
      - release*  # Will push to UAT without approval
  # Define what paths to include for building this pipeline. Normally, you should add the same projectPath as above,
  # so that any change to the project will trigger the pipeline, but it will ignore any changes to other projects.
  paths:
    include:
      - '*' # same as '/' for the repository root

# This points to the repository containing the templates. Update this to point to your template repository. Using the Concept of DRY.
resources:
  repositories:
    - repository: templates
      type: github  # use 'git' for a repo hosted in Azure DevOps
      endpoint: rpabotsworld
      name: rpabotsworld/UiPath-Azure-Devops 

# Use the above specified template for your project. 
extends:
  template: project-pipeline-template.yaml@templates  # Template reference
  parameters:
    projectPath: $(projectPath)
    folderName: $(folderName)
    versioningStrategy: $(versioningStrategy)
    environments: $(environments)

References

Ultimate CI/CD pipelines using UiPath GitHub Actions and PowerShell

Satish Prasad — Sat, 16 Apr 2022 17:34:05 +0000

In this article, we’ll explore the continuous integration and continuous delivery or deployment of UiPath based projects.

Well, CI/CD is not a new term now and it’s being adopted widely across all the organizations with a certain level of automation.

The goal here today is to see how we should be able to automate the delivery of our automation projects to deliver faster and with high quality.

There are several tools available to perform/set up CI/CD pipelines such as Jenkins, Azure pipelines etc. which provides similar functionality.

We have already covered: (See links)

CI-CD Using Jenkins –Implementing CI CD UiPath Using Jenkins Plugin – RPABOTS.WORLD (rpabotsworld.com)
CI-CD Using Azure Pipelines –Implementing CI CD Pipeline for UiPath – RPABOTS.WORLD (rpabotsworld.com)

In this tutorial, we will use the following tools: Git, GitHub, GitHub Actions, UiPath Orchestrator to set up complete using

First, we’ll cover some important terminology of GitHub Action and define our branching & CI-CD strategy
Second, we’ll set up continuous integration automatically run builds and tests using development workflow.
And finally, we’ll set up continuous delivery so we can automatically publish to the Production orchestrator using production workflow.
Some additional steps like slack notification

Alright, that was a lot.

Let’s get started!

Understanding GitHub Actions

GitHub Actions is a continuous integration and continuous delivery (CI/CD) platform that allows you to automate your build, test, and deployment pipeline. You can create workflows that build and test every pull request to your repository or deploy merged pull requests to production.

Let’s look at various terminology used in GitHub action for automating your pipelines.

Core concepts Used in GitHub Action

GitHub provides Linux, Windows, and macOS virtual machines to run your workflows, or you can host your self-hosted runners in your data centre or cloud infrastructure.
To automate a set of tasks, you need to create workflows in your GitHub repository. GitHub looks for YAML files inside of the. github/workflows directory.
Events like commits, the opening or closing of pull requests, or updates to the project’s wiki, trigger the start of a workflow. For example, a workflow is triggered when somebody pushes to the repository or when a pull request is created. Events can also be configured to listen to external events using Webhooks.
Workflows are composed of jobs.
Jobs are made up of multiple steps and run in an instance of the virtual environment. Jobs can run independently of each other or sequential if the current job depends on the previous job to be successful.
A step can be a set of shell commands or an action, which is a pre-built, reusable step implemented
Actions are the smallest portable building block of a workflow and can be combined as steps to create a job. You can create your Actions or use publicly shared Actions from the Marketplace.

If you have noticed the above description, you will see that it flows the YAML Markup language which is widely used in other CI/CD tools (Such as Azure) …

How to Set up GitHub Actions

GitHub Actions uses YAML syntax to define the workflow.

Each workflow is stored as a separate YAML file in your code repository, in a directory called. github/workflows.
You should be able to select a workflow from the ones available or create a new one. For this tutorial, we will create new.
To create your workflow, create a .yml file in the path .github/workflows/action1.yml. Commit the file, GitHub will automatically understand that it is a workflow.
For security’s sake, it’s important not to hard code secrets inside the codebase. A good way to avoid this is by using environment variables to refer to the secrets.

Open your repository on GitHub and go to the Settings tab. On the left navigation bar, click Secrets.
Here we can define any ORCH_URL, ORCH_TENANT, ORCH_CLIENT_ID, ORCH_USER_KEY, ORCH_ACC_NAME or ENV Specific Values.

Pretty easy right!

Let’s first understand the branching strategy and then we will create our workflow step by step.

GitHub Branching and CI/CD strategy [You can define as per need]

GitHub repository with two branches ‘master’ and ‘develop’ to track the UiPath project.
New ‘feature’ branch from develop branch (to perform changes)
pull request against the develop branch on GitHub
Two CI/CD pipelines (Separately for development and production using yml file configuration) to publish changes in the UiPath Orchestrator environment.
Development pipeline will run when pull request successfully merged with develop branch.
The production pipeline will run when the developed branch will be merged with the master branch.

The diagram below fully details this strategy:

Set up the GitHub repository for UiPath Project

Go ahead and log into your GitHub account. Click on the + sign in the top right corner, then click on new repository:
Let’s say – uipath-with-github-action (Name of Repository)
At this point in the article, we assume that you understand how to manage different branches using git client or UiPath Studio.
Next; Create a new project/or use the existing UiPath project and perform git init to initialize it locally
Now we need to set up a remote repository so that it can be tracked remotely. This can be done using UiPath Studio using manage Remotes
1. Give a Name – Say, Master
2. URL – https://github.com/rpabotsworld/uipath-with-github-action.git
The above will automatically create a master branch for you. Simply create a new branch called to develop with the following command using git client or UiPath Studio or “git checkout -b develop”
Now, let’s go back to our project on GitHub-actions- UiPath and click on Settings > Secrets > New repository secret, as shown in the screenshot below:
Now we’re done with our GitHub and local repo setup.

Set up GitHub Actions

Create a folder at the root of the project named (.github). Inside (. github) , create a new folder called workflows. Inside the workflows folder, create a file named development.yml
Similarly, create another workflow with name production.yml
UiPath Don’t provide any native plugin for GitHub Action so we have used the unofficial library of DevOps PowerShell scripts. which allow us to package, deploy and run automation and tests. (See Link here – UiPath-DevOps-Scripts )
The PowerShell scripts have a dependency on UiPath-dev – UiPath.CLI (Can be downloaded & set up via nuget/UiPath.CLI )

The overall structure of developement workflow

Understanding the development workflow file

uses: actions/checkout@v2: For us to run our PowerShell, we need to have it available. This checks out our code on our job environment so we can use it to build, publish &test.

Production Workflow File

Understanding the production workflow file

uses: actions/checkout@v2: For us to run our PowerShell, we need to have it available. This checks out our code on our job environment so we can use it to build, publish &test.

Summary

In this article, we’ve built a CI/CD process using Github actions.

We started by adding the CI process which runs the tests and tells our code reviewers that the Pull Request is ready for review.

There is so much more that can be done with GitHub actions. We hope this tutorial got you started with building a CI/CD process that is helpful and meaningful for your team.

References –

UiPath Code Review Checklist – To Perform Effective RPA Code Reviews

Satish Prasad — Thu, 25 Nov 2021 05:52:18 +0000

Code review can be treated as a check-post/ guardrails in which developers submit their code for feedback before merging branches or deploying code to production.

The feedback is usually given by colleagues, either other developers, a manager, or a tech lead.

In this article, I’ll aim to build your code review skills by suggesting the different elements you should consider when conducting one.

You can use this list as a checklist to go through when you’re reviewing code.

Let’s get started!

Introduction

Typically RPA developer opens a pull Request or he/she request a reviewer by creating/moving JIRA tickets to review the code by the Lead developer.

UiPath also provides the workflow analyser that performs the static code analyser and ensures that it follows the best practices.
However, it checks for inconsistency without actually executing the project, as opposed to dynamic analysers

“Workflow Analyzer uses a set of rules to check for various inconsistencies unrelated to project execution. The rules are based on Automation Best Practices and take into consideration variable and argument naming, empty sequences or workflows, package restrictions, and so on. The analyzer does not identify errors in execution or compilation.”

So keep in mind even if you perform Workflow Analyzer it still needs code review to be done by a peer developer or Team lead to ensure it’s ready for production use.

Read more details about Enforcing the Workflow Analyzer here.

The goal of code review

There are several benefits of performing code reviews, but each team can focus on a different matter. Typically, the purpose you can look for will be improving general code quality and reducing the number of bugs by sharing the knowledge of the author and reviewers. Also, developers will educate each other on how to write better code and understand business problems.

Provide better solutions
Early caught bugs
Improved overall code quality
Standardization of code rules and style
Two reviewers are better than one

Be Your Code Reviewer

First thing first; While reviewing the code, ask yourself the following basic questions:

Are you able to understand the workflows easily?
Is the workflow written following the coding standards/guidelines set by RPA COE?
Are there more appropriate ways of performing the task(Using activities or Custom codes)?
Is the same workflow duplicated more than twice?
Is there sufficient logging done for each workflow milestones activity?
Can I unit test / debug the code easy to find the root cause?

If you feel that the answer is not satisfactory to any of the above questions, then you can suggest/recommend code changes.

Code Review Checklist for UiPath Code Reviewer

The Code Review process, like any other process, can use some rules, especially in bigger and more complex projects. More developers in the team mean diverse experience and practices.

So it is the best practice that you should list and document the steps you follow during code review.

Here is a quick checklist you can rely upon by performing code reviews.

Implementation

Does this code change do what it is supposed to do?
Have the requirements been met?
Can this solution be more simplified?
Does this change add unwanted Uipath Package (UiPath or Third Party Supported) dependencies?
Is the Workflow modular enough?
Would you have solved the problem in a different way that is substantially better in terms of the code’s maintainability, readability, performance, security?
Does this code follow best practices and framework principles?
Does this code follow the Re-Framework or Template used by RPA COE?
Does this automation integrate with Third-party systems via API or Connectors?

Security and Data Privacy

Are authorization and authentication handled in the right way?
Our credentials are stored in the Credential store? Such as Orchestrator asset or Cyber arc
Is sensitive data like user data, credit card information securely handled and stored? Is the right encryption used?
Are shared folders used in automation have the right set of access controls?
Is data retrieved from external APIs or libraries checked accordingly?
Is there any PII related information logged in UiPath Orchastartor logs?
Is there Error screens captured with sensitive information?
Is the service account or Robot account has additional permission than Process SME?
How can we know what automation is used in the organization?

Readability

Is the UiPath workflow easy to understand?
Can the readability of the workflows be improved by smaller workflow xamls?
Are there redundant comments? Commenting on your code is fantastic; however, it can be overdone or just be plain redundant.
Is there any commented out code?
Are there deeply nested if-else or sequence blocks?
Are there pinned annotations or Comments at the top of each workflow, describing what the workflow is used for an explanation of each Argument to send In and Out?

Maintainability

Is Configuration is hard-coded?
Are all required configurable values are in (XML file, database table, Excel, txt ) so that no code changes are required?
Does it contribute to tech debt by increasing investment in a technology that the team wants to phase out? Such as the use of specific features of the old library?
Is workflow using pre-built RPA content provided by a third party? If any…are they supported by publishers?

Speed and Performance

Performance for users and resource consumption? Is there an activity using a high CPU?
When possible, code should use lazy loading and parallel processing
Use a data type that best suits the needs such as data tables, lists etc.
Is workflow using “for each”? Avoid doing it over a large no of records.
Use LINQ Queries. LINQ is commonly used to query arrays, data tables, dictionaries, lists, and XML. Arrays, dictionaries, and lists require no modifications or special treatment to act as a LINQ data source.

Scalability

Consider if it supports a large user transaction?
Consider if it supports Multi Bot architecture?

Reusability

Check that the workflow is written with likely future use-cases in mind
DRY (Do not Repeat Yourself) principle: The same workflow steps should not be repeated more than twice.
Is there are workflow that can be created as a Re-usable library ?

Extensibility

Easy to add enhancements with minimal changes to the existing workflow

Download UiPath Code Review Checklist

Conclusion

But most importantly, keep improving your code review checklist. The chances of you getting a perfect checklist the first time you build one are slim.

Find out which bugs have slipped through the review process and add the necessary checklist items to prevent them.

The above code review checklist is not exhaustive but provides a direction to the code reviewer to conduct effective code reviews and deliver good quality code.

Initially, it would take some time to review the code from various aspects. After a bit of practice, code reviewers can perform effective code reviews, without much effort and time.

If you would like to become an expert code reviewer, this code review checklist serves as a great starting point.

Happy Code Reviewing!

Working Example Using Robocorp Work Items | Definitive Guide

Satish Prasad — Thu, 11 Nov 2021 12:34:44 +0000

ROBOCORP DATA MANAGEMENT

Today I’m going to show you exactly how to work with Robocorp Data Management Work Items.

In this comprehensive guide, I’ll cover –

What is Work Items
Why Work Items & Benefit
Various Keywords required for Work Items management
Useful code snippet
Complete working example using Work Items concept
Lots more

So if you want to learn and start working with Robocorp Work Items, You’ll love this guide.

Let’s get started.

What is Work Items?

Work Item is the smallest chunk of data that can be processed completely end to end.
Each step of your BOT execution can receive work items as input (contains data & file) and Create work items as Output for the next step processing
You can relate work items as Traditional Loan application file that needs to be moved across different steps such as checking eligibility, chance of default, credit limits, and finally disbursement. Same file work as work items (input and optionally modified some values for next step)
A work item contains a payload of JSON-based data and optionally attached files. You can store anything that’s is serializable.
Adding and using files with work items requires no additional setup and by default, it’s stored in Robocorp control Room.

Benefits of work items

Splitting large volume of work into individual (small chunks) work items for better handling and parallel processing
Provide the ability to build business-critical large workloads process using a queue to complete processing in the parallel environment (Some say multi-Robot environment)
Provides ways for analytics and reporting for the individual piece of work
Work Data Management break up a process into separate work items so that the overall process can be tracked at a granular level providing a capability to re-process failed work items or handle manually
Enables dynamic scaling of runtime environments for parallel execution to significantly improve throughput
Enables processing huge volumes of data efficiently to complete the job faster and increase time-to-value
Work Data Explorer also provides business users with the tools to manage work items such as running, modifying, retrying, and marking work items as done according to different use cases.

Pre-requisite and dependency

Robocorp VsCode extensions has built-in support making the use and testing of work items.
Requires the use of rpaframework v11.4.0 or later in your robot.

Various Keywords in RPA.Robocorp.WorkItems

Add Work Item File
Add Work Item Files
Clear Work Item
Create Output Work Item
Delete Work Item Variables
For Each Input Work Item
Get Current Work Item
Get Input Work Item
Get Work Item File
Get Work Item Files
Get Work Item Payload
Get Work Item Variable
Get Work Item Variables
List Work Item Files
Release Input Work Item
Remove Work Item File
Remove Work Item Files
Save Work Item
Set Current Work Item
Set Task Variables from Work Item
Set Work Item Payload
Set Work Item Variable
Set Work Item Variables

Read more about the keywords here – https://robocorp.com/docs/libraries/rpa-framework/rpa-robocorp-workitems/keywords

How it works

As stated above work items are used for managing the data that passes through the various steps of automation. Where each step takes input from previous steps to create a work item for the next step.

Structure of Work items –

Work items are JSON means it allows you to store any kind of information that is serializable. In addition, you can also store files with work items.
Using the various keywords, you should be able to Create outputs work items, read the content of active work items, manipulate the content of current work items
You can create multiple work items as an output from a task, every time you use the keyword ‘Crete output work item’ a new empty item is created as a child for the currently loaded input, which overall makes it easier to track and move information for steps processing.
Work items become active when the execution started and the read and write from the work item will work on the active work item only, however, you can set the work item as active using the keyword ‘Set current work item’
Changes to work items are modified locally and can’t be reflected unless you use the keyword ‘Save work item’. So, make sure you save work items when you are done with the process of the dataset.

Example Snippet– Creating Work items from Excel file

Create Output Work Item creates a new empty work item.
Set Work Item Variable sets a new variable name and value into the work item.
Save Work Item saves the item and all contained data/files to the next step.

Work item exception handling

Exceptions are everywhere and we can’t assume that all the work items will be processed successfully, some might fail due to application issues when chrome crashes or any other HTTP error, some might fail due to business exceptions and further processing of the work item is not possible.

RPA.Robocorp.WorkItems Keyword libraries provide the keyword ‘Release Input Work Item’ to set the status of the active input work item.

So that status of each work item can be reported and analyzed.

The work items can be released with either “success” or “failure” using the keywords “DONE” and “FAILED”.

Saying “FAILED” is not going to help the operation team or Business Users so the additional exception details must be sent to the control room.

You can set up status as Filed with exception type, exception code if any, and message.

Exception Example snippet –

Developing with work items locally Using VS Code

When executing a robot in a cloud environment like Control Room, the RPA.Robocorp.WorkItems library will store the work item in the cloud environment, sharing its contents between steps defined in the same process, without any configuration needed.

However, when running locally, the work items can be simulated using folder structure and JSON files.

Robocorp VsCode extensions has built-in support making the use and testing of work items
As each task in the robot expects different work item input
The RPA.Robocorp.WorkItems library can be controlled with specific environment variables to control the input and output sources
By default, the env.json is used by Robocorp Lab so the inputs and output paths defined there decide which input is used.
In this example under ./devdata you can find different JSON files that demonstrate the selection:

To run specific tasks with specific inputs in the command-line or Robocorp Lab Terminal you can run the following commands:

Run Process Data:
- rcc task run -t “Process Data” -e ./devdata/work-items-out/run3/work-items.json

Complete Working Example: Cryptocurrency ticker with multiple work items

Robot Overview

This robot tracks the cryptocurrencies portfolio of customer from an incoming Excel file, the individual crypto coins is tracked for price movement is then handled individually in parallel in cloud container.

The robot demonstrates the concept of work items feature of Robocorp-

Creating multistep process
Passing data between the process steps using work items
Parallel execution of steps
Use of Custom Python Library (pycoingecko to get Crypto prices using Coin Gecko API)
Sending consolidated HTML email using python Jinja2 template engine

Robot Tasks

The robot is split into 3 tasks.

CreateWorkitems – Read Excel Records to Create Robocloud Work Items, Creates a new work item for each Crypto Coin.
ProcessWorkItems – Process Work items Individually to get Latest price from CoinGecko API, can be set to run in parallel
StatusWorkitems – Send Consolidated Report of All coins from Input in template-based HTML email.

Robocorp Library Used

Robocorp.WorkItems
HTTP
JSON
Excel.Files

Additional Python Library Used

Smtplib, jinja2 ,sqllite3
Pycoingecko wrapper for CoinGeckoAPI

Robot Input File - List of Crypto Coins

ID	Coin	Symbol
1	bitcoin	BTC
2	ethereum	ETH
3	tether	USDT
4	solana	SOL
5	cardano	ADA
6	xrp	XRP
7	avalanche	AVAX
8	litecoin	LTC
9	chainlink	LINK

CreateWorkItems.robot

ProcessWorkItems.robot

StatusWorkItems.robot

Simple Python Library for Email and Database

Robot Output Result - Consolidated Email

Download Working Example

GitHub

Important!

The above example can be improved using Robcorp Vault to store various credentials
The database part is optional, it’s for improvement as it can be used to plot graphs and charts from historical price movement to include in the email.

RPABOTS.WORLD

Mastering the Exam PL-500: Microsoft Power Automate RPA Developer

Introduction

Exam PL-500: Microsoft Power Automate RPA Developer Deep Dive

Key Modules in the Exam

Preparing for the Exam

Tips and Tricks for Success

Common Mistakes to Avoid

Benefits of Being a Certified RPA Developer

Career Opportunities Post-Certification

Real-world Applications of Power Automate

Comparing Power Automate with Other RPA Tools

The Future of RPA and Power Automate

Personal Experiences with Exam PL-500

Expert Interviews on Power Automate

Resources for Further Learning

FAQ Microsoft Power Automate RPA Developer

Conclusion

References

AI Builder in Power Automate: Harnessing the Power of AI- A Comprehensive Guide

Introduction to AI Builder in Power Automate

Understanding Power Automate

AI Builder: The Game Changer

What is AI Builder?

AI Builder Capabilities

Key Features of AI Builder in Power Automate

Prebuilt AI Models

Custom AI Models

Seamless Integration

Exploring AI Builder in Power Automate

How to Use AI Builder in Power Automate

Step 1: Access AI Builder

Step 2: Choose a Model

Step 3: Train your AI model

Step 4: Apply to Power Automate

Step-by-Step Guide to Using AI Builder in Power Automate

Example Working: Extract data from an invoice document and save it in an Excel

Setting up AI Builder

Building Flows with AI Builder

Real-World Applications of AI Builder in Power Automate

How AI Builder is Revolutionizing Industries

Business Process Automation

Data Processing and Analysis

Customer Engagement

Advanced Techniques in AI Builder for Power Automate

Optimizing Workflows with AI Builder

Advanced AI Models in Power Automate

Future Prospects of AI Builder and Power Automate

Challenges and Solutions in Implementing AI Builder in Power Automate

Frequently Asked Questions

1. Can anyone use AI Builder in Power Automate?

2. Can I use my own data with AI Builder?

3. How is AI Builder different from Power Automate?

4. What are some applications of AI Builder in Power Automate?

5. Are there any limitations to AI Builder in Power Automate?

Conclusion: Transforming Business Processes with AI Builder in Power Automate

References :

ServiceNow and UiPath Communication Mining Example: Enhancing ITSM Efficiency

Problem Statement: ServiceNow Auto Ticket Triaging

Solution : Automating ticket triaging with UiPath Communication Mining

Working Example: How UiPath Communication Mining will Work

Stage 1: Preparatory Work, Data Collection and Preparation:

Stage 2: Create DataSet, Different Settings for Language and Entity:

Stage 3: Working with Data Sources, Upload via CSV

Stage 4: Explore Train and Apply Labels

Using labels, entities and metadata together

Some Background Details Labels, entities, and metadata

Overview of the model training process

Stage 5 :Validation, Report & Pin the Model

Stage 6: Using the Communication Mining Model in UiPath Studio Project

Get predictions from a pinned model example​

Stage 7: Designing End-to-End Automation Using the Trained Model & Other Uipath Components

Summary:

Implementing UiPath Communication Mining: Step-by-Step Guide

Implementing UiPath Communication Mining:

Uipath Communication Mining Key Concepts:

Setting Up UiPath Communication Mining Account:

Understand Permission in UiPath Communication Mining Account:

Navigating in UiPath Communication Mining Account:

AI Units and Licensing Metering and Charging Logic:

Get predictions from a pinned model example