The increasing use of artificial intelligence (AI) in software development is one of the hot topics in recent months. It is expected that soon there will be flood of AI-generated code, which will make the development phase of a product faster, albeit with the possibility that it may entail technical debt. I will leave this ongoing discussion to developers.

Here, my aim is to look into the potential opportunities and challenges that the use of AI would bring to the testing part of the development process.

Let’s start with the opportunities:
Firstly, AI can help write test cases faster and automate many repetitive and time-consuming tasks, particularly regression testing. This, in turn, can help reduce time, cost and manual effort. This will allow the testers to focus on more complex and critical areas of testing, rather than spending time on repetitive tasks.

Secondly, AI can help identify defects more accurately and quickly, and find the defects that might be missed by human testers. This will lead to more thorough and comprehensive testing and better overall quality of the software.

Third, AI can help predict which parts of the software are more likely to have issues, enabling testers to focus their efforts on those areas.

Last, but not least, AI can be used to automate testing as part of a continuous integration and delivery (CI/CD) pipeline, providing real-time feedback on the quality of the software.

What about the challenges?
First of all, AI-powered systems can be highly complex and difficult to understand, making it more difficult to ensure that they are functioning as intended. This requires specialized testing methods and techniques to identify any issues with the AI-powered systems.

Second, these systems are highly dependent on data, and any errors or biases in the data used to train the AI system can have a significant impact on the performance of the system.

Thirdly, these new tools may miss issues that a human tester would notice, such as usability issues, which can lead to a less-than-optimal user experience.

And, of course cost. The cost of implementing AI-powered testing tools and training staff to use them can be higher than traditional manual testing methods.

In conclusion, it is safe to say that the demand for testing is likely to increase to cope with the flood of code, and to ensure the quality and reliability of the AI systems.

While AI-powered testing can be a powerful tool, it is not a replacement for human testers. Humans are still needed to design test cases, interpret results, and ensure that AI-powered testing tools are functioning properly. Accordingly, the demand for skilled testers is likely to grow as more and more companies adopt AI technology in their development process. For this reason, the response to AI technology in testing could be one of welcoming and adaptation, rather than worrying about its negative implications.