What AI future holds for testers

The buzz of Artificial intelligence has definitely gotten you as tester as it has to many engineers and professionals around the IT world. What would be 2024 has in bag for testers with AI in perspective? I asked same question back in 2018–19 when cloud usage became so imperative and must have, that every software solution and professional needed to get acclimated to in order to stay relevant.

As any professional, staying and upskilling with evolving time not only gives you leverage for growth but also gets you ahead of curve. Since 2020 , AI has been in observability phase until in last yr or so when we start to see simulation and democratization in form of new new chatbots and tools which claim to seamlessly integrate with your existing test automation setup to make you more productive as testers if anything. Given the lack of case studies at this point to prove ROI, there is lot of hesitation to invest in AI for testing. Having said that, we are not far off from world where it will become important piece of puzzle to up your quality game and enable you to produce at pace way more than human could.

Most important use of AI and ML( machine learning) from testing perspective that market is leaning on are but it comes with word of caution I.e the conclusions reached by an AI algorithm are only as good as the data sets it feeds on:

1. Predictive Analytics : Generation of tescases based on specifications and use of applications in production,
2. Self healing automation: learning over time how to triage effectively, fix and adjust test based on product. Saving money and time for hefty test maintenance
3. Increasing coverage: identifying gaps and adding more tests like adhoc/ monkey testing, wider security test coverage
4. Enables shift right testing in Production to let your business focus on later part of devops cycle ( add devops cycle image)

As much thrilling it all sounds, there are many factors that businesses and professional needs to evaluate before embarking on AI journey for effective transformation. It’s essential to understand few pitpalls that you need to be looking out for:

• Use cases you potentially looking AI to rely on or solve for you
• Time period to implement fully reliable solution
• Value gained/outcome during estimates period vs investment
• Teams will slow down in releasing new feature initially as they indulge in AI and ML learning
• Expect to tune solution with time based on data feed
• Be sure to check and be cognizant of Governance and Legal aspect

All in all, testers aren’t going anywhere as long as they stick around and adapt to new evolution. If you have thrive or even survive in past against news dreading but cool trends, YOU will so yet again!

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