AI-powered test automation: Discover how AI language models like Groq, Llama, and Mistral assist in generating self-healing test scripts, reducing manual intervention and maintenance.
Self-healing tests: Learn how the integration of AI can fix failing tests automatically by adjusting to changes in the application, minimizing the need for constant test updates.
Optimising efficiency: Explore how this setup can improve efficiency by automating error detection, code suggestions, and script updates.
After reading, we’d love to hear from you:
Have you tried integrating AI into your test automation workflows? Share your experiences or challenges with self-healing tests
Isn’t using an AI losing control of what you’re testing, how, and why?
How is the risk managed when giving AI the power over a good piece of automation code?
Is the AI responsible for the automation or the tester?
Using the self-healed code, is the automation engineer experienced enough in those software development concepts to manage that going further?
The author seems to create an equivalence between testing = automated checks = AI (which can repair my crappy automation)
In the described implementation you as a tester do not lose any control of testing.
how?
Creation:
All the automation code is generated from your manually written test cases, so if you want to increase coverage you can always add more manual tests.
What you gain ?
You no longer need to write automation tests.
But only the test cases, so the risk management is still in your hands.
Risk management for “good automation code”?
Firstly, i love this comment of yours , this gave me another idea, of integrating github with it. Because you want to have some control, as whenever a new tech comes in , we have to be careful with it.
What we can do it create pullrequests when a fix is created so an engineer can review it. What do you think will that help?
Is the automation engineer experienced enough?
Probably not, and that brings me to a point that the more AI gets involved in our life the more everyone will have the need to know a bit about it, at least at surface level.
Please do not confuse this with replacing testers or testing, this is only to say that AI can help in small places but is not at a place where it can replace the whole testing industry.
AI-powered test automation: Discover how AI language models like Groq, Llama, and Mistral assist in generating self-healing test scripts, reducing manual intervention and maintenance.
Self-healing tests: Learn how the integration of AI can fix failing tests automatically by adjusting to changes in the application, minimizing the need for constant test updates.
Optimising efficiency: Explore how this setup can improve efficiency by automating error detection, code suggestions, and script updates.