Test Automation in the World of AI & ML

Key Takeaways
There are many criteria to be considered before building framework / selecting tools for Functional Test Automation
It is very important to prioritise framework / tools capabilities needed for the software-under-test
A good, scalable Test Automation Framework that provides fast and reliable feedback to the team enables collaboration and CI/CD
Debugging / RCA (root cause analysis) and support for libraries / tools used is an afterthought in most cases. Do not fall in that trap.
There are some promising commercial tools that fit seamlessly in the Agile way of working. Depending on the complete context, these tools may be a good choice over building your own framework for Functional Automation.
Artificial Intelligence and Machine Learning, fondly known as AI & ML respectively, are the hottest buzzwords in the Software Industry today. The Testing community, Service-organisations, and Testing Product / Tools companies have also leaped on this bandwagon.

While some interesting work is happening in the Software Testing space, there does seem to be a lot of hype as well. It is unfortunately not very easy to figure out the core interesting work / research / solutions from the fluff around. See my blog post – “ODSC – Data Science, AI, ML – Hype, or Reality?” as a reference.

One of the popular theme currently is about “Codeless Functional Test Automation” – where we let the machines figure out how to automate the software product-under-test. A quick search around “codeless test automation” or, “ai in test automation” will show a few of the many tools available in this space.

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