Taking your Test Automation to the Next Level with AI and ML

Eti Sharma
Published
Machine learning is widely used in test automation as a pattern recognition technology. It works with those templates that are determined by algorithms and predicts future trends. ML can process huge amounts of complex information, find patterns, classify data, create predictions, and warn of possible risks or discrepancies. Read on to know more. By– Prabhat Ranjan, Sabhyata Gurung, and Aditya Bhan Giri
system testing using machine learning
The basic principle of machine learning is that machines take data and learn based on them. Many processes such as face, speech, object recognition, and translation, etc use systems of machine learning successfully.

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.
reinforced learning

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.
machine learning for testing automation

Popular Testing Automation Tool based on AI/ML technology:

  1. Selenium
  2. Code Intelligence
  3. Functionize
  4. Testsigma
  5. Katalon Studio

Summary

In summarization, AI/ML is no longer a new word in test automation, but a kind of necessity. Artificial intelligence and machine learning can speed up release times, provide better analytics, and in addition helps you find the root causes of failures and fix them. AI/ML is also used in test writing, test coverage assessment, and UI testing. Even though the technology has its drawbacks, but a competent QA team is capable of coping with them. For sure, the test automation platform is important here. Lastly, the quality of future results depends on how well the AI/ML model is implemented there, and how easily it learns and adapts to the customer’s requirements. Get in touch with Telenity to explore customizable platforms for Employee trackingFleet tracking, and  location based APIs for your business at [email protected]

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