Self-Healing Tests

Detailed explanation

Self-healing tests, also known as adaptive tests or resilient tests, represent a significant advancement in automated software testing. Traditional automated tests often rely on brittle locators (e.g., XPath, CSS selectors) to identify UI elements. When the application's UI changes, even slightly, these locators can break, causing test failures and requiring manual intervention to update the tests. Self-healing tests aim to mitigate this problem by automatically adapting to these changes, reducing test maintenance overhead and improving the overall reliability of the test suite.

The core principle behind self-healing tests is to use intelligent algorithms and techniques to identify UI elements based on multiple attributes and contextual information, rather than relying solely on a single, potentially fragile locator. When a test fails due to a locator issue, the self-healing mechanism attempts to find alternative locators or strategies to identify the target element.

How Self-Healing Works

The self-healing process typically involves the following steps:

1. Test Execution and Failure: A test is executed, and an element is not found using the initially defined locator, resulting in a test failure.
2. Locator Analysis: The self-healing engine analyzes the failed locator and the surrounding context. This may involve examining the element's attributes (e.g., ID, class, text), its relationship to other elements in the DOM (Document Object Model), and the overall structure of the page.
3. Alternative Locator Generation: Based on the analysis, the engine generates a set of alternative locators or strategies for identifying the element. This might involve using different locator types (e.g., CSS selectors instead of XPath), combining multiple attributes, or using relative locators (e.g., "the button next to the text field with label 'Username'").
4. Locator Validation: The generated locators are then validated to determine if they correctly identify the target element. This can be done by querying the DOM using each locator and verifying that the returned element matches the expected characteristics.
5. Test Repair and Re-execution: If a valid locator is found, the test is automatically updated to use the new locator, and the test is re-executed. If the re-execution is successful, the self-healing process is complete.
6. Reporting and Learning: The self-healing engine typically provides reporting on the changes it made, allowing developers to review and approve the updates. It also learns from these experiences, improving its ability to identify elements in the future.

Practical Implementation and Tools

Several tools and libraries support self-healing testing, including:

• Testim: A cloud-based testing platform that uses AI to automatically identify and fix broken tests. It learns from each test run and adapts to changes in the application.
• Mabl: Another cloud-based testing platform that offers self-healing capabilities. It uses machine learning to identify elements and automatically update tests when the UI changes.
• Selenium with AI-powered locators: Some Selenium-based frameworks incorporate AI-powered locator strategies that can automatically adapt to UI changes. These strategies often use a combination of attributes, text, and visual cues to identify elements.
• Custom Implementations: It's also possible to implement self-healing capabilities using custom code and libraries. This typically involves using a combination of DOM analysis, machine learning, and heuristics to identify elements.

Example (Conceptual):

Let's say a test uses the following XPath locator to find a button:

//button[@id='submit-button']

If the ID of the button changes to submit-button-new, the test will fail. A self-healing engine might analyze the failure and try alternative locators, such as:

• //button[text()='Submit'] (locating the button by its text)
• //button[contains(@class, 'btn-primary')] (locating the button by its class)
• A relative locator based on a nearby element (e.g., "the button next to the 'Password' field").

If one of these alternative locators is successful, the test can be updated to use the new locator, and the test can be re-executed.

Best Practices

• Combine Self-Healing with Good Test Design: Self-healing should not be used as a substitute for good test design. Tests should still be written with clear objectives and well-defined assertions.
• Review and Approve Changes: It's important to review and approve the changes made by the self-healing engine to ensure that the tests are still valid and that the correct elements are being targeted.
• Use Multiple Locator Strategies: When defining initial locators, consider using a combination of attributes and contextual information to make the tests more resilient to changes.
• Monitor Test Stability: Track the number of self-healing events to identify areas of the application that are prone to UI changes. This can help prioritize development efforts to improve the stability of the UI.
• Provide Feedback to the Self-Healing Engine: If the self-healing engine makes incorrect decisions, provide feedback to help it learn and improve its accuracy.
• Consider the Performance Impact: Self-healing can add overhead to test execution. It's important to consider the performance impact and optimize the self-healing process to minimize any slowdown.

Advantages of Self-Healing Tests

• Reduced Test Maintenance: Self-healing tests significantly reduce the amount of time and effort required to maintain automated tests.
• Improved Test Stability: Tests are less likely to break due to minor UI changes, resulting in a more stable and reliable test suite.
• Faster Feedback: Developers receive faster feedback on their changes, as tests are less likely to fail due to locator issues.
• Increased Test Coverage: With less time spent on test maintenance, testers can focus on writing new tests and expanding test coverage.

Disadvantages of Self-Healing Tests

• Potential for Incorrect Fixes: The self-healing engine may sometimes make incorrect decisions, leading to tests that pass but do not actually validate the intended functionality.
• Increased Complexity: Implementing and maintaining self-healing tests can add complexity to the testing process.
• Performance Overhead: Self-healing can add overhead to test execution, potentially slowing down the testing process.
• Dependency on AI/ML: The effectiveness of self-healing tests often depends on the accuracy and reliability of the underlying AI/ML algorithms.

In conclusion, self-healing tests offer a promising approach to improving the efficiency and reliability of automated software testing. By automatically adapting to UI changes, they can significantly reduce test maintenance overhead and provide faster feedback to developers. However, it's important to carefully consider the potential drawbacks and implement self-healing in conjunction with good test design practices.

Further reading

• Testim Self-Healing Tests: https://www.testim.io/blog/self-healing-tests/
• Mabl Autorepair: https://www.mabl.com/autorepair
• DZone - Self-Healing Test Automation: https://dzone.com/articles/self-healing-test-automation