A Journey towards self-healing Test Automation

In the ever-evolving landscape of software development and testing, test automation has become an indispensable tool to ensure the quality and reliability of applications.

However, one persistent challenge in test automation is dealing with false positive failures caused by broken UI locators, which can hinder efficiency and increase maintenance efforts and, most importantly, dramatically reduce the reliability and trust on the test automation actual results by the whole development team, product owners and business stakeholders.?

To address this issue, the concept of self-healing test automation has emerged, which includes a solution covering the broken UI locators to also dynamically repair them.

In this short text,, we will explore what self-healing test automation means in regards to the problems associated with broken locators, the need for a systematic process, strategies involving multiple locators per UI element, and the self-updating code. Additionally, we will look into the potential integration of AI in this domain.

The Challenge of Broken Locators

Locators are crucial in test automation scripts as they specify how to locate elements on a web page or application interface, so that the automation can interact with the and verify the expected consequent behaviour of the application.

However, applications frequently evolve, resulting in changes to the structure of elements. When locators become outdated due to these changes, automated tests may fail, even though the application is functioning correctly. This phenomenon can generate false positive failures, leading to wasted time and resources as developers investigate non-existent issues, and damage the trust and reliability of the test automation as an actual oracle to define the actual health and correct functionality of an application.

The Need for a Systematic Process

To combat the problem of false positive failures due to broken locators, a systematic approach to test automation is essential. Self-healing test automation involves the creation of tests in a way that anticipates locator changes and can adapt to them without manual intervention. Here are some key steps in implementing a self-healing process:

- Locator Strategy: Rather than relying on a single locator for an element, employ multiple locators (e.g., ID, XPath, CSS selector) to identify the same UI element. This redundancy ensures that if one locator breaks, the others can still locate the element

- Locator Validation: Regularly validate locators against the application's current state. If a locator is found to be broken, it should trigger an automatic process for replacing it with a spare locator.

- Self-Updating Code and Process: Implement a mechanism that recognizes when a locator has failed and automatically switches to a spare locator. This ensures that the test script continues to function smoothly despite changes in the application.

The process should take care to automatically update the broken locator with a spare one, and also identify and update a spare one so that the process can repeat over time by always having, per each UI element, a set of always working locators (the main one and the spare ones).

Such update of the locators should be performed automatically by the test automation by pushing the code into the (for instance git) repository and notifying the team about such update (see the next item).

- Logging and Reporting: Maintain detailed logs and reports to track the status of locators and their replacements. This transparency aids in debugging and monitoring the health of your test suite.

- Outlook on AI in Self-Healing Test Automation

Artificial Intelligence (AI) holds great promise for enhancing self-healing test automation. AI algorithms can be employed to:

- Predict Locator Changes: Machine learning models can analyse historical data to predict when and where locator changes are likely to occur. This proactive approach can help testers address issues before they cause failures.

- Automated Locator Generation: AI can generate and suggest alternative locators for UI elements when the primary locator fails. This reduces manual effort in maintaining locators.

- Dynamic Element Recognition: AI can assist in recognizing UI elements dynamically by learning their behaviour and patterns, reducing the reliance on static locators.

Conclusion

Self-healing test automation represents a significant advancement in the world of software testing, including addressing the persistent issue of false positive failures due to broken locators. By adopting a systematic approach, including multiple locators per UI element and self-updating code, organisations can improve the efficiency and reliability of their automated testing processes, leading to saving precious resources like time and money.

Furthermore, the integration of AI technologies offers a promising outlook for the future of self-healing test automation by providing predictive capabilities and dynamic adaptation to evolving applications, ultimately enhancing the quality of software products.