Modernise Software Testing with Generative AI Tools

By: Vuyiswa Mahlasela

The biggest revolution to the way software testing is being carried out today relies on AI and machine learning advances. Generative AI has proven to be a disruptive innovation that is changing the software testing landscape.

Generative AI tools allow us to create new content or data based on existing information. As far as software testing is concerned these tools can help in creation of test cases, simulate user actions and predict any potential upcoming software issues. This enables QA teams to automate repetitive tasks, achieve larger test coverage and reduce the overall time required for the test cycles.

Traditional testing tools force testers to spend significant hours writing scripts and manually inputting commands. In contrast, generative AI allows you to write tests in plain English. Their natural language processing capability allows for both technical and non-technical team members to take part in the testing process, which increases collaboration and efficiency.

Generative AI tools such as TestRigor learns how an application works, creates test scenarios that are able to cover multiple user cases. By automating these processes, not only can your teams be more efficient when supporting customers as they utilise the product, but it also guarantees test coverage for complicated scenarios and lower chances of production bugs.

Top Benefits of Generative AI In Software Testing

Increase Efficiency: Reduce Time on Test Creation & Maintenance with Generative AI. With tools like TestRigor, QA teams can focus more on improving test strategies rather than spending significant hours writing scripts

Lowers Input/Effort: While previously generative AI was able to reduce the time in test coverage, now it reduces effort from input, generating higher quality software.

Enhanced Collaboration: Generative AI offers the ability to create tests in plain language and encourages participation in QA from team members who might shy away due to lack of skills, fostering greater collaboration while also encouraging shared accountability on product quality.

Predictive Analysis: By analysing historical data from previous phases, generative AI can identify patterns and predict areas of risks, allowing teams to act before issues become critical and improving software stability.

Continuous Testing: Continuous testing is necessary due to the frequent updates in code. Generative AI helps this by enabling rapid test creation and execution, allowing QA teams agility with ongoing changes while quickly identifying problems.

Let’s consider a scenario involving an application for a web based business application. The development team is tasked with rolling out new features regularly to enhance user experience and continually add value to the applications users. However, with each new feature, the QA team faces the challenge of ensuring that existing functionalities remain intact while thoroughly testing the new changes. Using generative AI tools can automatically create tests that simulate multiple user interactions, accelerating the testing process but also ensuring that all critical user journeys are validated.

As the application evolves, generative AI tools can continuously update the test cases based on changes in the applications structure and functionality. This adaptability means that the QA team spends less time maintaining outdated tests and more time focusing on strategic testing activities.