Can AI make software testing a breeze?

Why hasn't there been a big breakthrough in AI testing yet? As of now, there isn't a perfect testing robot that can start working on an IT project right away, even if you pay for it. AI-created unit tests are not flawless, and they don't always give the expected results on the first try. For example, when we ask ChatGPT4 to multiply two big numbers, it doesn't give the exact answer, which is not good for testing financial software.

 

To make the most of AI, we need to spend time and money on training machine learning models, which might not always be worth it. This involves using much data to get good results, which can be hard for small and medium businesses. Even big companies like Amazon have found that using AI for testing can be limited by time and computer resources.

How can we get better at testing? Right now, we seem to be more interested in testing AI than using it to its full advantage. Before we use AI for automatic test creation, we need to understand the basics of testing.

 

Jumping into using AI without planning can lead to bad habits. But, if we know what we want, AI can help us create certain parts of code faster, like test models based on database structures, especially with complex data. If we give AI a lot of written instructions, it can offer many test cases for us to pick from. We can also ask it for common solutions to simple testing problems. This makes AI more of a helper than a partner in testing.

As we navigate the ever-changing landscape of the IT sector, it's evident that AI holds a significant place in shaping the future. However, it's not about replacing human expertise but complementing it. AI is a powerful tool, aiding in various aspects of software testing, from generating test cases to offering insightful suggestions. For newcomers in the testing field, it acts as a gateway to a wealth of knowledge, albeit with a need for deeper exploration.

Moving forward, it's vital to approach AI with a balanced perspective, understanding its capabilities and limitations. By fostering a collaborative relationship with AI, we can pave the way for more efficient and innovative solutions in software testing. Ultimately, the goal is to harmonize the strengths of both human intelligence and artificial intelligence, creating a synergy that propels the IT sector to new heights.