How is AI changing the software testing landscape? | by TestVagrant | TestVagrant | June 2022


The QA space is constantly evolving with high-end technological innovations. One of the most dominant trends reshaping software development and testing is artificial intelligence and machine learning. The blog explores how AI is influencing software testing practices. Give it a read.

How is artificial intelligence redefining software testing?

1. Predictive analysis

In today’s competitive digital landscape, customer focus is at the heart of product development. Understanding customer needs is important to staying ahead of the competition. This is where predictive analytics plays a crucial role.

With time and technological advancements, AI has acquired the ability to process collective data and make intelligent predictions. Machine learning (ML) is an important aspect of AI that takes advantage of pattern recognition technology. It discovers patterns in the data provided and uses them to predict future trends.

Additionally, the AI ​​can predict and equip the app for real-time scenarios. Here is a use case to better understand. In an e-commerce application, users can add items to the cart from the product detail page or the homepage. AI may recommend that the checkout process be tested from both pages.

AI is constantly evolving by processing more and more data sets and behavioral patterns and thus improving its capabilities. Together, AI and ML improve the efficiency of testing techniques and help improve the user experience.

2. Reliable localization strategies

Forbes points out in AI in software testing “Development teams no longer have the luxury of the time they had when monthly product releases were the rule of thumb. Today’s businesses demand weekly releases and updates that arrive even more frequently. . »

In this time-constrained environment, the market demands efficient automated testing tools, especially during the later stages of product development. While human intervention and manual testing aren’t going away anytime soon; automated testing solutions make the process easier.

Platforms like Testim, Testsigma, Test.AI, etc. are among the providers of automated testing solutions. Artificial intelligence feeds these platforms in real time, analyzes the DOM objects of a page and extracts the objects with their properties.

The AI ​​decides the best localization strategy to locate a particular item based on the scan. This is why even if a developer changes an element’s attribute, the test continues to run. This leads to more stable tests. The AI ​​also helps with localization testing and ensures that the behavior of the product matches the specific region.

3. Visual validation

AI powers visual testing and allows test engineers to leverage image-based learning and screen comparisons to test the aesthetics and functionality of any application. There are several ML-based visual validation tools that help detect complex UI anomalies.

These tools help recognize dynamic UI controls regardless of their shape and size and analyze them at the pixel level. Thus, these ensure that all visual elements are attractive and highly functional.

Applitools is one such visual testing and monitoring tool. It can be easily integrated with all modern testing frameworks, build servers, source code control and collaboration tools.

BrowserStack’s Percy is another all-inclusive visual review platform that is equipped with different features such as pixel-by-pixel differences, snapshot stabilization, and responsive differences. The tool has parallelization capabilities and ensures high-speed, cross-browser rendering.

These visual AI tools help test engineering teams reduce the amount of test code they rewrite, the time they spend running and fixing tests, and the time they spend analyzing results.

4. Improved test coverage

AI helps increase test coverage by checking file contents, data tables, internal program states, and memories. AI in software testing can perform different test cases in a single test, providing a wide range of coverage. It also helps in analyzing whether the program is working as expected and providing effective test coverage.

5. Specify test domains and speed time to market

Intelligent AI predictions can recommend which aspects of the product should be tested. These recommendations allow test engineers to pay attention to areas that might otherwise be overlooked. Thus, AI-enabled smart recommendations help reduce overall testing time.

Additionally, AI-driven testing facilitates continuous testing. Continuous testing is the process of performing automated testing as part of the software delivery pipeline where changes are constantly tested and delivery is streamlined. As a result, products get to market faster and companies can get to market sooner.

Is AI replacing human test engineers?

AI has undoubtedly brought revolutionary changes to software test engineering. By giving test engineers a break from manually writing everything, AI helps speed up the whole process. However, human intervention cannot be entirely replaced. It’s important to think of an AI platform as a tool to optimize software testing, but not as the ultimate.

Simon Steward, the creator of Selenium WebDriver, says in an interview with Joe Colantonio: “You don’t want to run (automate) everything because you don’t know if it’s working or not. We should have a clarification at this point. We shouldn’t do such blunt trauma to our codebases.

Depending on the requirements of end users, test engineers should adjust the use of AI. Manual testing should be integrated into the testing infrastructure as needed.

This is where a software testing consulting company can come help. Consultants and test teams can suggest and run various types of tests and meet business requirements.


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