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Troubleshooting Procedures

Error Messages and Logs in AutomatR Studio​

Error messages and logs are crucial tools for diagnosing and troubleshooting issues that may arise during the development and execution of automation workflows in AutomatR Studio. They provide valuable information about errors, warnings, and unexpected behavior, allowing developers and automation engineers to pinpoint and resolve issues effectively.

1. Error Messages:

Error messages are notifications generated by AutomatR Studio when an issue or error occurs during the design or execution of an automation workflow. These messages often include error codes and descriptions that provide specific details about the nature of the problem. Here are some key points to consider when dealing with error messages:

  • Error Codes: Error messages frequently include error codes that uniquely identify the type of error. These codes can help you look up specific information related to the error in AutomatR's documentation or community forums.

  • Error Descriptions: The error descriptions accompanying error codes offer context and details about what went wrong. Reading these descriptions carefully can provide insights into the root cause of the issue.

  • Error Severities: Error messages are often categorized by severity levels, such as "Error," "Warning," or "Information." Understanding the severity level helps you prioritize and address issues accordingly.

  • Stack Trace: In some cases, error messages may include a stack trace, which is a detailed list of function calls and events leading to the error. This information can be invaluable when debugging complex issues.

2. Logs:

Logs are records of events, actions, and information generated during the execution of automation workflows. They serve as a historical record of what transpired during a workflow run. In AutomatR Studio, logs are instrumental for monitoring and troubleshooting automation processes. Here's how to work with logs effectively:

  • Types of Logs: AutomatR Studio provides various types of logs, including Execution Logs, Output Logs, and Trace Logs. Each type serves a different purpose and provides specific information.

  • Monitoring Execution: Execution Logs capture information about the execution of activities, including the start and completion of each activity, status, and timestamps. These logs help track the flow of the automation process.

  • Output Logs: Output Logs record the output of specific activities, such as extracted data or values. They are useful for validating the results of data extraction or transformation activities.

  • Trace Logs: Trace Logs offer detailed debugging information, including variable values, arguments, and function calls. They are essential for in-depth troubleshooting and understanding the workflow's behavior.

3. Utilizing Error Messages and Logs:

To effectively use error messages and logs in AutomatR Studio:

  • Read Thoroughly: Start by carefully reading error messages and logs. Pay attention to error codes, descriptions, and timestamps to understand the issue's context.

  • Filter and Filter Levels: AutomatR Studio allows you to filter logs by various criteria, including severity levels and keywords. Use filtering to focus on specific errors or events.

  • Debugging: When encountering errors, use logs, especially Trace Logs, in combination with debugging tools like breakpoints, variable inspection, and immediate window to identify and resolve issues efficiently.

  • Documentation: If you encounter error codes or issues that are not immediately clear, refer to AutomatR's documentation.

In conclusion, error messages and logs in AutomatR Studio play a pivotal role in the development and troubleshooting of automation workflows. By checking error messages and logs diligently, you can diagnose issues, identify root causes, and implement effective solutions, ultimately ensuring the reliability and efficiency of your automation projects.

Activity Configuration in AutomatR Studio​

Activity configuration is a critical step in building efficient and accurate automation workflows in AutomatR Studio. Properly configuring activities ensures that they perform the intended actions and produce the desired results within your workflow. This section provides an in-depth overview of activity configuration in AutomatR Studio, covering the key aspects and considerations.

1. Adding Activities:

  • Begin by adding activities to your workflow from the Toolbox - Activities panel on the left. Drag and drop activities onto the design canvas to build your automation process.

2. Activity Properties:

  • Each activity has a set of properties that can be configured. These properties define how the activity behaves during execution. The properties may include:
    • DisplayName: The name displayed on the activity.
    • Variables: Input and output variables that store data.
    • Arguments: Input and output arguments used for data exchange with other activities.
    • Timeouts: Specify how long the activity waits for a response.
    • Selectors: Define the target element in applications (for UI automation).
    • Condition: Set conditions for activity execution.
    • Input Parameters: Customize input data.
    • Output Parameters: Define output data.

3. Input Arguments:

  • Input arguments are used to pass data into an activity. It's essential to ensure that the input arguments are correctly configured to provide the necessary data for the activity's execution. Input arguments can be variables, constants, or expressions.

4. Output Arguments:

  • Output arguments are used to pass data out of an activity. Define and configure output arguments to store the results or data produced by the activity. These outputs can be used later in your workflow or passed to other activities.

5. Activity Configuration Best Practices:

  • When configuring activities, follow these best practices:
    • Provide meaningful display names to make your workflow more readable.
    • Review and verify the activity's properties, ensuring they are set up correctly.
    • Document your workflow with comments to explain the purpose of each activity.
    • Use variables and arguments to store and manage data, promoting reusability and readability.
    • Handle exceptions by adding Try-Catch activities or exception handling mechanisms where appropriate.

6. Data Validation:

  • Perform data validation checks to ensure that the data used in activities is accurate and within the expected range. Implement conditional statements and error-handling to address potential data issues.

7. Testing and Debugging:

  • Test the workflow by running it in debugging mode. Use breakpoints to stop the execution at specific points and inspect the values of variables and arguments. This helps identify and resolve issues.

8. Activity Documentation:

  • Use the activity documentation feature to add notes and descriptions to activities. This aids in understanding the purpose and functionality of activities, making it easier for team members to collaborate on automation projects.

9. Validation and Review:

  • Regularly review and validate the properties and configurations of activities to ensure they align with your automation goals and business requirements.

10. Reusability:

  • Whenever possible, design activities and workflows with reusability in mind. Create custom libraries, arguments, and templates that can be used across multiple projects.

By diligently reviewing and configuring the properties and configurations of individual activities within your workflow, you can build robust, efficient, and accurate automation solutions. This attention to detail ensures that your AutomatR Studio workflows perform as expected and contribute to improved business processes.

Selector Validation in AutomatR Studio​

Selector validation is a crucial step in UI automation with AutomatR Studio. Selectors define how automation activities interact with elements in the user interface, and validating and fine-tuning them ensures that your automation reliably targets the correct elements. This section provides a comprehensive overview of selector validation in AutomatR Studio, including tools and best practices.

1. Understanding Selectors:

  • A selector is a string of information that identifies a specific UI element within an application. It includes attributes like the element's name, type, position, and more. Selectors are used by automation activities to interact with UI elements.

2. Importance of Selector Validation:

  • Accurate selectors are vital for the reliability and accuracy of UI automation. Validating selectors ensures that the automation interacts with the intended UI elements.

3. Tools for Selector Validation:

  • Selector Window: This tool allows you to edit selectors manually, making adjustments and verifications.

4. Steps for Selector Validation:

  • Access the Selector Window:

    • Open the workflow in AutomatR Studio.
    • Locate the activity that involves UI automation and contains a selector. Double-click the activity to open it for editing.
    • Within the activity properties, find the selector field and click on the "..." button to access the Selector Window or use the "Select" option.

  • Review the Selector:

    • Examine the selector displayed in the Selector Window. Ensure that it accurately identifies the desired UI element by checking the attributes.

  • Fine-Tune the Selector:

    • If the selector needs adjustments, use the Selector Window to modify the attributes manually. Ensure that you include attributes that are both unique and stable.
    • In UiExplorer, you can explore the UI hierarchy, selecting the specific UI element that should be targeted. UiExplorer will automatically generate a selector for you.

  • Validation and Testing:

    • After fine-tuning the selector, use the "Check/Highlight" button to test whether the selector can locate the UI element accurately.
    • Test the automation by running the workflow in debugging mode and monitoring the interaction with the UI element.

5. Selector Validation Best Practices:

  • Choose selectors that are resilient to changes in the UI, such as those based on unique attributes like ID or a combination of attributes.
  • Minimize the use of dynamic or changing attributes in selectors to maintain stability.
  • Use wildcards (* and ?) judiciously in selectors to allow for flexibility when necessary.
  • Regularly review and validate selectors to accommodate any changes in the application's UI.

6. Documentation:

  • Document your selectors and their fine-tuned configurations to make it easier for team members to understand and maintain the automation.

7. Exception Handling:

  • Implement exception handling mechanisms to address scenarios where selectors may fail, ensuring that your automation can gracefully handle such situations.

Selector validation is an ongoing process that requires attention to detail and thorough testing. By validating and fine-tuning selectors in your AutomatR Studio automation workflows, you can build robust and reliable automation solutions that accurately target and interact with UI elements, contributing to improved business processes and increased efficiency.

Variables and Data in AutomatR Studio​

Variables and data management play a crucial role in AutomatR Studio. They are used to store, manipulate, and transfer information during the execution of automation workflows. Properly managing variables and data ensures the accuracy and reliability of your automation. This section provides a comprehensive overview of variables and data in AutomatR Studio, including best practices and debugging tools.

1. Variables in AutomatR:

  • Variables are containers that store data. They can be used to hold information such as text, numbers, dates, and more. AutomatR offers various types of variables, including:
    • Generic: Holds a wide range of data types.
    • Argument: Used for passing data into and out of workflows.
    • System: Stores system information.
    • Asset: Holds sensitive or reusable data in a secure manner.

2. Importance of Variables:

  • Variables are essential for:
    • Storing and managing data during workflow execution.
    • Passing data between activities and workflows.
    • Making decisions, calculations, and data transformations.

3. Data Handling in AutomatR:

  • Data can be obtained from various sources, such as user inputs, web scraping, files, and databases. It is essential to validate, transform, and use data correctly within automation processes.

4. Debugging Tools:

  • AutomatR Studio provides debugging tools to inspect variable values and data during workflow execution. One such tool is the "Locals" panel, which allows you to monitor variables and their values in real-time.

5. Using the "Locals" Panel:

  • To utilize the "Locals" panel for variable and data inspection:
    • While running the workflow in debugging mode, open the "Locals" panel.
    • The panel displays the current values of the selected variables, making it easy to track how data changes throughout the workflow.

6. Best Practices for Variables and Data:

  • Naming Conventions: Use descriptive variable names that convey their purpose and content, promoting readability and maintainability.

  • Data Validation: Implement data validation checks to ensure that data is accurate and within the expected range.

  • Type Safety: Use variables with appropriate data types to avoid data type mismatches and errors.

  • Clear Documentation: Add comments and documentation to explain the purpose and usage of variables. This aids in understanding the workflow for yourself and others.

  • Data Security: If handling sensitive data, consider using SecureString or securely storing data using Assets in Orchestrator.

7. Data Processing Issues:

  • The "Locals" panel helps identify data processing issues, such as incorrect calculations, invalid inputs, or unexpected data values. It allows you to spot issues in real-time and make necessary adjustments.

8. Exception Handling:

  • Implement exception handling to address data-related errors and failures, ensuring that your automation can gracefully handle unexpected situations.

9. Data Transformation:

  • Use data manipulation activities in AutomatR to transform data, such as text manipulation, mathematical operations, and date-time conversions.

By following best practices for variables and data management and utilizing debugging tools like the "Locals" panel, you can build automation workflows that effectively handle and process data. This attention to data quality and monitoring helps ensure the reliability and accuracy of your AutomatR Studio automation projects.

Exception Handling in AutomatR Studio​

Exception handling is a critical aspect of building robust and reliable automation workflows in AutomatR Studio. It allows you to gracefully manage errors and unexpected conditions that may arise during workflow execution. This section provides an in-depth overview of exception handling in AutomatR Studio, including best practices and the use of Try-Catch blocks.

1. Understanding Exceptions:

  • Exceptions are events or conditions that disrupt the normal flow of your automation. They can occur for various reasons, such as application changes, missing data, network issues, or unexpected user behavior.

2. Importance of Exception Handling:

  • Effective exception handling is crucial for ensuring that your automation workflows continue to operate smoothly, even when unexpected errors occur. It helps maintain reliability and accuracy.

3. Exception Handling Mechanisms:

  • AutomatR Studio offers several mechanisms for handling exceptions, including:
    • Try-Catch Blocks: These are structured activities that encapsulate one or more activities in a "try" block and specify how to handle exceptions in the associated "catch" block.

4. Implementing Proper Exception Handling:

  • Use Try-Catch Blocks:

    • Wrap activities that are prone to exceptions within a "Try" block. This isolates the potential errors to a specific section of the workflow.

  • Identify and Handle Specific Exceptions:

    • Within the "Catch" block, specify how to handle specific exceptions that are likely to occur. For example, you can handle "ElementNotFound" exceptions or "ApplicationException."

  • Log and Notify:

    • In the "Catch" block, log details of the exception using the "Log Message" activity and consider sending notifications or alerts to relevant stakeholders.

  • Custom Exception Handling:

    • Create custom exception types or error codes to provide more specific information about the nature of the exception.

  • Rethrow Exceptions:

    • In some cases, you may want to rethrow exceptions to handle them at a higher level in your workflow or report them to a central exception tracking system.

  • Retry Mechanisms:

    • Implement retry logic within the "Catch" block to handle transient errors or network issues by trying the same action multiple times.

5. Best Practices for Exception Handling:

  • Use Descriptive Exception Messages:

    • Write informative and clear exception messages that describe the nature of the error. This helps in troubleshooting.

  • Separate Exception Handling Logic:

    • Keep exception handling logic separate from the main workflow, making the workflow more readable and maintainable.

  • Testing and Validation:

    • Thoroughly test and validate your exception handling mechanisms by simulating various error scenarios to ensure they work as expected.

6. Documentation and Commenting:

  • Document your exception handling strategies and add comments to the workflow to explain how exceptions are handled.

7. Error Reporting and Logging:

  • Implement error reporting and logging to track exceptions, making it easier to analyze and resolve issues.

Exception handling is not a one-time effort but an ongoing process. Regularly review and update your exception handling mechanisms as your automation evolves and as new exceptions may arise. Properly implemented exception handling ensures that your AutomatR Studio automation workflows can gracefully manage errors and continue to deliver reliable results.