TMS + Git: Driving Transparency in AI-Powered Testing

Just a couple of years ago, the testing process seemed well‑established and straightforward, but everything changed with the advent of artificial intelligence in our lives. AI agents have significantly sped up our routine tasks, but they have also introduced many mysteries and uncertainty in key questions: “Who made the changes?”, “Why do we need three duplicate cases?”, and, finally, “Why was the bug missed?”.

And it’s not about the quality of the models. With a good context, AI is quite capable of performing good checks. The problem is that we cannot check and verify exactly what it did.

During our report, we will discuss:
• How we streamlined the testing process by breaking it down into several stages.
• How storing cases in Git and the “Tests-as-Code” approach helped us with this.
• Why the TMS continues to play a key role in ensuring quality.

We will also define the areas of responsibility: what can be entrusted to AI, and what still needs to be handled by a person.