There was a time when software testers didn’t have any option other than manually testing their applications. They had to manually run the scenarios being tested and document the results in a testing software so that the developers could resolve them. This went on for many years until the advent of automation in testing. Automation allowed testers to run test scripts automatically and that helped accelerate the overall testing process. Testers could now run the scripts any number of times by just hitting a run button and the steps for testing a functionality of an application would execute automatically. This was a major improvement when compared to manual testing being conducted before that. But as history suggests, technology never stops evolving; there’s always a chance for more and more improvements and enhancements.
Comes the era of AI – it filled up the gaps which even automated testing could not do so far. Although automated testing was able to run test scripts automatically, it lacked the capability of adaptability. Software often goes through frequent changes and it is a pain for the testers to keep updating their test cases to suit the new updates in the software. This pain-point is exactly what AI resolved by introducing self-healing capability. What AI-powered self-healing tests brought to the table is that they can adapt themselves to the frequent changes being introduced to the software without any manual intervention. These systems are smart enough to update their test-scripts on their own according to the functionality changes in the software to test them effectively. In this article, we examine how this feature of AI testing is revolutionizing the test automation strategies for software companies and what are the benefits they provide.
Understanding AI-powered Self-healing Tests
Most of the free AI testing tools use machine learning algorithms to identify any changes in the applications and update the test-script accordingly on their own. They don’t apply any major complex concept for self healing but use a similar algorithm that we call “common sense” in our language. What they actually do is look beyond just the hard-coded programming language and try to make sense of the changes based on the attributes instead.
For example, if there is a change in the name of the login button from “btn-logn” to “logn-btn”, traditional automated testing would fail because it depends on the hard-coded language and even a slightest change in the code would cause the test script to fail if it was not updated. But the self-healing AI testing software, like testRigor, would not just look into the name but also the attributes of this button, like label text, nearby elements, HTML structure, etc., and would understand that it is the same button which was used previously. Hence, it would still run successfully without any update in the test script for the name change in the button without any human intervention.
Working Mechanism
The way it works different from traditional automated testing is that it uses specialized features which give it more flexibility to analyze, operate, decide and execute. These features provide autonomy to the AI system to run the test cases on its own. Let’s briefly look into these features:
- Element Fingerprinting: When the test cases are created initially, the AI testing software doesn’t just store hard-coded test cases. It also saves multiple properties of the UI elements like XPath, CSS selectors, text labels, position, size and other metadata. This helps in providing input to the system for a wider analysis.
- Change Detection: While testing, if primary locators are not met with the test script, the system looks for the alternative attributes to continue the testing process. This allows more flexibility to the system to run the test cases on their own.
- Confidence Scoring: While looking at the alternative attributes, the machine learning algorithm assigns a probability score to those alternatives. If they cross above the threshold, it decides to run the test cases. This scoring mechanism is especially useful in automated decision making capability of the AI testing software.
- Automated Healing: The attributes, which allowed the system to run the test cases, get stored for future runs. So, when the next time the test case is run, it doesn’t have to spend its resources on repeating the same analysis it has already done previously. In other words, the test cases once evolved remain evolved for future use.
Benefits of AI-powered Self-healing Tests
So far we have seen the inner workings of AI powered tests, but we must understand the overall benefits of it beyond the obvious ones. Let’s look at them one by one.
- Easy maintenance: This is the most obvious benefit of self-healing capability of AI testing software. Because of its adaptability feature to the frequent product updates, there’s no need for manual intervention in updating the test cases. This allows for easier maintenance of test suites for future use.
- More test stability: Since the test cases become more intelligent by considering relevant attributes as well, they become more stable and less prone to fail due to minor changes. This allows the testers to focus more on the testing results rather than spending time on updating the test cases themselves thereby enhancing the quality assurance process.
- Accelerated testing: When test cases don’t break as often, the overall testing process becomes much faster. All the stages of testing can pass much quickly without any hassle due to the new changes introduced into the software.
- Saves time and cost: Due to faster and accelerated testing process, the cost involved in the testing stage of the software development process reduces dramatically. Teams have to spend less time on repetitive and low-value tasks that accounts for a major reduction in the overall cost of the project.
Final Thoughts
So far, AI-powered self-healing testing has reached only the surface levels of the software testing. It is mostly confined to the UI and visual testing. But as test automation software is evolving, the self-healing capabilities are spreading towards more complex areas like API and database validation. Also, the human-AI collaboration in self-healing capability is becoming more feasible through GenAI algorithms. These testing software are becoming smarter day by day, adapting to the needs of the quality assurance and providing the features and functionalities that can be leveraged to its highest potential. If you have not yet considered using AI-powered testing tools, then the best time to get acquainted with it and start using it is no better than now.
