Self-Healing Test Automation: A Key Enabler for Agile and DevOps Teams

Test automation is essential for ensuring the quality of software products. However, test automation can be challenging to maintain, especially as software applications evolve over time. Self-healing test automation is an emerging concept in software testing that uses artificial intelligence and machine learning techniques to enable automated tests to detect and self-correct issues. This makes test automation more reliable and cost-effective and reduces the amount of time and resources required to maintain test scripts.

In this article, we will discuss the benefits of self-healing test automation, how it works, and how to implement it in your organization.

What Is Self/Auto-Healing Test Automation?

Self-healing test automation is a new approach to test automation that uses artificial intelligence (AI) and machine learning (ML) to make test scripts more robust and adaptable. With self-healing test automation, test scripts can automatically detect and repair themselves when changes are made to the application under test, including shifting layouts and broken selectors. This makes it possible to automate tests for complex applications with frequently changing user interfaces without having to constantly maintain and update the test scripts.

Why Is Self-Healing Test Automation Necessary?

Test automation scripts can easily break when changes are made to the user interface. This is because test automation scripts are typically designed to interact with specific elements on the screen, such as buttons, text fields, and labels. When these elements change, the script may no longer be able to find them or interact with them correctly. This can lead to test failures and false positives, which can be time-consuming and frustrating to resolve.

Also, the user interfaces are constantly evolving, with new features and bug fixes being added frequently. This means that test automation scripts need to be updated regularly to adapt to these changes. However, updating test automation scripts can be a manual and time-consuming process, which can be challenging to keep up with the pace of change.

Self-healing test automation addresses this fragility of traditional test automation scripts by adapting to changes in the user interface automatically to make test scripts more robust and adaptable. Self-healing test scripts can automatically detect and repair themselves when changes are made to the application under test, which can help to reduce test maintenance costs, improve test quality, and increase test coverage.

How Does Self-Healing Mechanism Work?

• Step 1: The self-healing mechanism gets triggered whenever “NoSuchElement” or a similar error occurs for an element mentioned in the automation script.
• Step 2: The algorithm analyzes the test script to identify the root cause of the error.
• Step 3: The algorithm uses AI-powered data analytics to identify the exact object in the test script that has changed. An object can be any interface item like a webpage, navigation button, or text box. 
• Step 4: The algorithm updates the test script with the new identification parameters for the affected object(s).
• Step 5: The updated test case is re-executed to verify that the remediation has been successful.

How Self-Healing Test Automation Adds Value to Your Software Delivery Process

Leveraging self-healing capabilities allows test automation to adapt to changes, improving test coverage, reducing maintenance efforts, and enabling faster feedback.

Saves Time and Effort

Self-healing test automation can save organizations a significant amount of time and effort in software testing. Traditional test automation approaches require manual intervention to fix errors or failures that occur during test execution. This can be a time-consuming and error-prone process, especially when dealing with large and complex test suites. Self-healing test automation eliminates the need for manual intervention, allowing tests to recover automatically from failures or errors.

Improves Test Coverage

Self-healing test automation can help to improve test coverage by allowing testers to focus on writing new tests rather than maintaining existing tests. This is because self-healing tests can automatically adapt to changes in the software under test, which means that testers do not need to spend time updating their tests every time the software changes.

As a result, testers can focus on writing new tests to cover new features and functionality. Self-healing automation can improve test coverage by 5-10% by eliminating unnecessary code, resulting in shorter delivery times and higher returns on investment.

Prevents Object Flakiness

Object flakiness is a common problem in test automation, especially for GUI testing. Object flakiness occurs when a test fails because it is unable to locate an object on the page. This can happen for a variety of reasons, such as changes to the UI, changes to the underlying code, or network latency.

Self-healing test automation can detect and prevent object flakiness by analyzing test results and identifying patterns that indicate flaky tests. By preventing object flakiness, teams can reduce the number of false positives and negatives, improving the overall accuracy and reliability of test results.

Faster Feedback Loop

Self-healing test automation also enables a faster feedback loop. With traditional test automation approaches, tests are often run manually or through a continuous integration pipeline. However, with self-healing test automation, tests can be run continuously, providing immediate feedback on the quality of the application under test. This enables teams to identify and fix issues faster, improving the overall quality and reliability of the application.

Conclusion

In Agile methodology, applications are continuously developed and tested in short cycles. This can make it difficult to maintain test cases, as the application is constantly changing. Self-healing test automation can help to overcome this challenge by automatically updating test cases when the application under test changes.