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Will AI Perform Testing?

Many testers often wonder whether AI (Artificial intelligence) will threaten their existence in the future. This topic becomes even more important in the context of today’s tech-driven world where a huge number of software companies are steadily adopting high-end technologies like AI and ML. In this post, we’ve tried to find out will AI be able to perform testing ever and if not, how it’ll improve different aspects of testing.

Testing is a crucial process that ensures customer satisfaction of an application and helps to prevent potential failures that may bring in some negative aspects down the line. It’s a planned process where an application is reviewed and analyzed under specific conditions to comprehend the overall threshold and hazards involved in its application.

By assimilating machines that can mimic human behaviors meticulously, testers can surely move beyond the traditional path of manual testing models and move forward progressively toward a precision-based and automated continuous testing process. An AI-powered testing platform is capable of recognizing changed control more efficiently compared to humans and with continuous updates to its algorithms, slightest changes can be observed.

These days, a major challenge stems from the increase in complexity, both in systems, as well as, the system landscape, and it demands more testing. Also, the testing has to go faster as we experience more frequent deliveries. Therefore, we’ve to be more efficient and it means that we need to leverage better tools and technologies as much as possible. Business intelligence has been around for a long time in different areas, but not heavily in the testing landscape. But these days, we should try to leverage business intelligence support for testing. We should try to implement AI in areas where it can perform better than humans and let the tester do what humans are better at.

You may wonder, what kinds of tasks should we let AI do? Machines are usually good at performing repetitive tasks provided they come with clear what to do and what outcomes to expect. For example, analyzing a massive amount of data is something machines are good at, and with ML capabilities, it can support the analysis performed by humans for whom it’s not possible to go through that amount of data.

On the other hand, humans understand other humans better because nuances in human communication are something highly difficult for AI to grasp. Humans are also better at adapting the communication based on the cues from the listeners. So, it’ll probably take a long time for AI-powered machines to understand the subjective knowledge and properly communicate to the stakeholders.

Here’re some ways AI will impact the testing domain heavily.

  • It’ll change the perspective of testing and thus, will make the lives of testers a whole lot easier. We can expect to see a trend where human testers will have to do a significantly less amount of mechanical dirty work that revolves around executing, implementing, and analyzing test results. However, they’ll still be an integral part of the entire testing process to approve as well as act on the findings.
  • Another important thing to understand is that the problems solved with AI aren’t deterministic and they change when the systems incorporate new data. There’ll be an array of possible outcomes and tester would need to run a test several times to ensure that the conclusion is correct statistically. This way of testing sounds more thought-provoking, more experimental, and more mathematic.
  • Another major aspect that is often talked about is how an AI-powered system would ensure the correctness of what is under test. Humans accomplish this by finding different sources of truth – a stakeholder, a product owner, a customer etc. Testers would require a different set of skills to develop and maintain AI-powered test suites which test AI-powered products. The requirements would involve more focus on skills related to data science and testers would need to have a sound knowledge of some deep learning principles.

Conclusion

AI is certainly good at performing some specific tasks, sometimes even better than humans. But a tester performs an array of different tasks which means that an AI-powered system needs to perform all those tasks in order to be able to do complete testing. While probabilities of this happening in the near future are pretty low, AI will certainly help greatly in expanding the capabilities and efficiencies of testers.

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