How Can Machine Learning Improve Software Testing

Companies expect their applications to function without errors. However, the software testing process is traditionally time-consuming and subject to human errors. Nowadays, QAs are lucky enough to have Machine Learning and Artificial Intelligence to make their job much easier.

Machine learning and AL allow specialists to integrate automated testing into their testing process and not only reduce the time spent on the process but also human participation. It requires manual work for defining the test case in the form of a programming script and the automated tools take the rest of the work.


Thanks to testing automation, software testing and maintenance is a more efficient and reliable process. Machine Learning allows for better testing results with a fraction of the labor. For example, testers no longer need to re-write test cases for individual scenarios.

Today, we’re looking at how you can use Machine Learning to improve your testing and maintenance strategy. Read on for more information.

Machine Learning/Artificial Intelligence in Test Automation?

Machine Learning and app testing automation can create a dependable testing strategy that benefits testers and companies alike. However, before you decide to implement Machine Learning into your testing strategy, you should consider the following things.

• Automated User Interface (UI) Testing

The user interface makes or breaks whether an application proves popular with application users. While website visuals may look fascinating, testers can easily miss certain broken functions on the page. As such, Machine Learning proves its worth here, using image recognition technology to verify and identify bugs in the user interface.

• Working on Unit Tests

Machine Learning can help testers create unit test cases, so testers do not need to waste time writing code unnecessarily. In addition, Machine Learning-based unit test scripts become handy later on in the product life cycle when tests need to be repeated.

• API Testing

However, API testing makes the traditional test and maintenance more difficult, requiring testers to understand how the API functions and formulate relevant test cases and scenarios.

Machine Learning allows you to record API traffic and activities to analyze metrics and create tests. However, if you want to update or modify those tests, you need to get your head around Representational State Transfer (also known as REST) calls and parameters.


• Numerous Test Scripts

One of the biggest benefits of using Machine Learning and Artificial Intelligence is predicting whether an application requires numerous tests. Traditionally, testers would need to change their test scripts whenever there was an upgrade, update, or code change. Machine Learning helps avoid creating unnecessary test cases, making the testing process more efficient and less costly.

• Artificial Intelligence and Machine Learning-based Test Data Generation

Artificial Intelligence relies on datasets, and test scripts need inputted data to run correctly. Testers can use Machine Learning during test automation to generate these datasets, so the test case works as though it is dealing with a genuine user. This information comes from trained Machine Learning models that use pre-existing production datasets to adapt.  These datasets resemble production data necessary for software testing.

• Robotic Process Automation for Regression Testing

Robotic Process Automation lets testers automate IT systems while maintaining them simultaneously. RPA scans the screen, looks through the system and functions, identifying and collecting data. Robots power this process, so every task is automated and runs via phone or web applications.

The biggest benefits of RPA are cost-saving, improved productivity, accurate outputs, and future scalability.


Future for Machine Learning and Software Testing Automation

Currently, we have only begun to leverage the possibilities of Artificial Intelligence and Machine Learning in test automation. Both technologies are still in their infancy and offer exciting potential that’ll make the test automation process even more efficient in the future.

Some of those exciting opportunities and applications expected to be available in the future include:

  • Machine Learning will eventually help automated testing become the number one strategy, making manual testing practically obsolete. While manual testing will still exist in some form, companies should choose more automated practices for efficient testing.
  • Accurate testing results and quality will become expected in most organizations. Artificial Intelligence and Machine Learning take control of all testing functions, generating and executing unit test cases in a much shorter time frame at a fraction of the cost.
  • Eventually, testers will solve the issue of too many or too few test cases thanks to Artificial Intelligence-based test creation tools. These intelligent functions will simplify the testing process for coders and testers.
  • Predictive Test Selection is also touted to become more prominent for organizations moving forward, especially when dealing with huge datasets. Currently, testing a small change can take days for IT companies to get relevant feedback. Predictive Test Selection may process modifications and run unit test cases that are likely to falter.


Finally, companies will save more money and time by combining Machine Learning and test automation. Testers are already seeing the benefit in this regard, as companies seek to use test automation, Artificial Intelligence, and Machine Learning across more departments.


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About the Author


Maria Harts is a QA specialist at Functionize and had previous work experience in the same position for 6 years. She is an enthusiastic specialist who wants to share her knowledge of the sphere with others and help them to increase their interest in IT.
Find out more about @mariaharts

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