What is the horizon for the future when we would see AI-backed testing/automation jobs in the market?
How will Gen AI hamper learning for QE, which happens at an early stage?
How can AI-driven testing tools help in predicting potential software defects before they occur, and what are the implications for software development lifecycles?
Are there any specific LLMs suggested for software testing purposes?
How is AI transforming the traditional roles and responsibilities of testers?
Is a QA Engineer depending on GenAI losing their testing mindset or skills?
How can we trust Gen AI even as a supplementary tool when hallucination and nebulous data are still such massive problems? What benefit do I have from using it when I’ll have to double-check everything it does?
How do you envision GenAI transforming the daily tasks of testers in the near future?
How does the proliferation of untrained LLMs, with other economic factors, contribute to unemployment rates in various industries, and what strategies can businesses implement to mitigate these effects?
So in the future, will we manage AI for software testing?
How does AI-driven testing differ from traditional automated testing methods?
Do we really have Gen AI test cases for all types of testing? If yes, again, are these AI all trained by the algorithm provided by humans?
What are the key challenges if we start working on generative AI?
How can manual testers uplevel their skills in the new AI era?
Historically, I’ve seen “generated test cases” requiring having requirements in Jira/Bug system and formatted in a specific way. Does newer test case generation still require things like that?
How can AI tools improve the testing process for better user experiences?
Does AI tools include accessibility and usability testing aspects
One should be well-versed with prompt engineering to use AI platforms efficiently.
Do we have a trial version from Wipro/Lambdatest which we can use for initial analysis?
I think to avoid skill atrophy, testers should actively engage in continuous learning, focusing on critical thinking, problem-solving, and domain knowledge. AI should be seen as a tool to complement, not replace, human expertise.