AI and ML in Testing: X Tips To Make Test Automation Effective

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December 15, 2021 Business

Artificial intelligence (AI) and Machine learning (ML) are paving the roads in software testing. The success of all IT projects depends on their accurate working. Therefore, the testing stage plays a vital role in the software development life cycle (SDLC). In recent years, automated testing has become a staple among software development. Using it significantly cuts the testing time frame with better quality testing and higher efficiency and helps them continuously deliver complex features in CI/CD environments.

Other advantages of using test automation include handling repetitive tasks, generating relevant data for decision making, and early detection and correction of bugs, amongst others. However, traditional automating testing tools like Selenium have some flaws. Therefore, these open-source tools need to change their approach towards automated testing.

Machine learning in software testing provides better and stable test execution and increases the reliability of the test results. As a result, companies can achieve greater efficiency, transparency, and speed in automation testing. As companies adopt AI-based automated testing tools, they may uncover the many benefits of AI test automation on their business lines. This article talks about some handy tips to make test automation effective by using AI and ML in Testing.

1. Machine Learning Tool Must-Know User’s Action

Fast and easy test automation creation is one of the problems and objectives companies look for in any Machine-learning supported testing tool. However, machine learning algorithms need a lot of data to learn user behavior on any web application. Therefore, to make test automation effective, AI-based automated tools should have a way to gather as much data as to understand the application behavior better. For example, Netflix uses artificial intelligence to know data of a user’s watch patterns to recommend content in the future.

2. Reducing Maintenance Cost & Saving Time

Manual testing can be time-consuming, unmanageable to maintain, and prone to human errors. Machine learning in testing is an extensive improvement from traditional automated testing. It is an excellent choice to perform repetitive tasks. AI enhances test automation further by offering Self-healing tests with a better analysis in the execution of tests. As a result, companies are willing to pay for the tool that can help them reduce the test maintenance cost and assist in having stable and robust tests capable of delivering quality results and increasing their business values.

3. Handling of UI Changes

Frequent changes to an application under test (AUT) often trouble QA testers and are their greatest pain points. Even small UI changes can show noticeable problems. Therefore, it is best to let the Machine learning tool recognize changes and alert the tester rather than wasting time learning test failure during analysis, investigating, and updating the test scripts. Some AI-powered test automation tools do not require coding to master like Functionize. This tool does not let the UI tests fail because of the UI changes. Instead, AI-based tools automatically detect these changes and propose alternatives to update the tests.

4. Grip on Big Data

Big data is one of the most eminent benefits of transitioning from manual testing to an AI test automation platform. ML testing tools must support handling large amounts of data to learn. The more the testing tool learns about the data patterns, the more stable the test scripts. This grip on big data will also identify bugs quickly, handle enormous quantities of data by keeping the development costs lower and provide predictive forecasting to make better data-driven decisions.

5. Machine Learning in Testing

As technology grows, writing automated tests is also evolving tremendously. The trend of no-code test automation is increasing. Product managers and business analysts can write the tests with the help of AI-based automation tools. To achieve this, the testing tool vendors have adopted machine learning at deep levels. For example, Google, Amazon, and Airbnb use machine learning to test their applications. The AI and ML-based tool should support NLP techniques in easier test creation, testing for multiple platforms and browsers, more straightforward analysis, and flexibility in performing different testing types.

Bottom Line

Machine learning in testing is both our future and probably the present. It is compelling and helps solve complex problems we didn’t think were possible a few years back. You can use it for your advantage, but you need to consider the specifics of what will make the model tremendous and of value.

As highlighted in this article, ML in Testing helps significantly handle application UI changes and reduces the maintenance time. In addition, it also assists in managing enormous data by learning the patterns and producing more stable tests.

Lastly, test maintenance nightmares can be avoided with the possibility to enhance the usage of machine learning in tests one step further. Test automation using AI and ML can help execute the tests over more expansive areas because ML learns the patterns and uses the data to improve the reliability of the tests. In testing software applications, AI and ML are a space worth watching. There is a lot more potential for innovation and growth.