AI in testing
- Thread starter jolkie4
- Start date
ZekeYer6
New member
Your skepticism is valid, and you’re not alone. There’s definitely a lot of noise out there, and many teams struggle to separate real capabilities from inflated promises. However, some AI testing tools do provide tangible benefits—but it’s crucial to distinguish facts from myths. There’s a great article that breaks this down clearly: AI based testing myths. It covers what AI can actually do in software testing today, what it can’t, and how to cut through the buzzwords to find tools that offer real, proven value.
johnsonmanila74
New member
Testing may be understood as a process that could measure knowledge, performance, or system reliability, depending on context and purpose. It might provide an opportunity to identify strengths and possible gaps, offering a chance for improvement rather than a final judgment. In education, a platform for students could use testing to track progress and highlight areas that may need more focus. This approach can create possibilities for growth, helping learners build confidence over time. When designed thoughtfully, testing may support development and encourage continuous learning instead of pressure.
Thempanince02
Member
Skepticism here is understandable—there's a lot of marketing hype surrounding AI in testing. They often promise to "automate everything and replace QA," though in practice, such claims are greatly exaggerated.
But if you take a sober look, there are already some useful use cases. AI is quite good at generating test cases, analyzing logs, finding potentially unstable (flaky) tests, and accelerating regression. It's just not "magic," but rather an add-on to existing processes that provides efficiency gains when used correctly.
For example, there are tools like AtomicBot that focus specifically on applied test automation—test generation and maintenance, CI/CD integration, and reducing routine work for QA teams. It doesn't replace engineers, but it can significantly reduce the burden on routine tasks.
So the bottom line is simple: there's a lot of hype, but the technology isn't empty—its real value simply comes from targeted implementation without expecting "miracles."
But if you take a sober look, there are already some useful use cases. AI is quite good at generating test cases, analyzing logs, finding potentially unstable (flaky) tests, and accelerating regression. It's just not "magic," but rather an add-on to existing processes that provides efficiency gains when used correctly.
For example, there are tools like AtomicBot that focus specifically on applied test automation—test generation and maintenance, CI/CD integration, and reducing routine work for QA teams. It doesn't replace engineers, but it can significantly reduce the burden on routine tasks.
So the bottom line is simple: there's a lot of hype, but the technology isn't empty—its real value simply comes from targeted implementation without expecting "miracles."