How do ML algorithms work in test automation?
How do ML algorithms work in test automation?Supervised learning
The machine receives training datasets with correct answers. The set includes data with information about the number of commits, tests and their results, code coverage, releases, the coverage of features, and fixed bugs. Hence, this machine learning algorithm is often used to measure potential risks.Unsupervised learning
The machine makes decisions by working with demarcated inputs. The algorithm used helps to identify the possibility of an error, as well as to understand if the data in the same cluster are like each other and simplify the data.Reinforced learning
Under reinforced learning the, quality assurance engineers train the neural network on the reward and punishment mechanism. In case of an error, the work of the function is corrected so that the next time it chooses the true option. Which means that, the more you teach the neural network, the fewer mistakes the system makes.
What AI and ML can bring to your test automation: Key benefits
Simplified test script creation
AI/ML is able to create complex test scenarios, and this makes the job of QA engineers easier. Moreover, this technology can convert manual test cases into automated ones. AI also creates reusable components to run data-driven tests and integrate CI/CD systems with both private and public grids. As a result, testing becomes more reliable and requires less effort to maintain, as test coverage grows.Reduced test automation cost
High-quality test automation tools aim to reduce the time to run and process tests, fix bugs, and provide analytics and reports. Also, AI/ML technology helps to prevent probable errors, automatically determines the reasons of failures, and makes it possible to identify their root causes. The reduction in costs, in this case, comes at the expense of saving the time that QA and development teams would spend to independently find the reasons for test failures.Accelerated product release
Test automation with built-in AI/ML technology works in parallel with the QA team. AI/ML accelerates the execution of automated tests and optimizes the entire testing process. In turn, QA engineers can focus on those types of testing that cannot be automated.Speed up recognition of failure reasons
This technology helps classify completed tests and speeds up the verification process. AI/ML is used for smart analysis, which, based on the results of the tests, indicates the most probable cause of failure. The model can be trained and thus improves its accuracy to the maximum possible. You can also see the most common test failure reasons and take steps to ensure that similar test issues do not occur again in the future.
Popular Testing Automation Tool based on AI/ML technology:
- Selenium
- Code Intelligence
- Functionize
- Testsigma
- Katalon Studio
