Quality Revolution: How AI & ML Reshape Assurance
QA Innovator, 18+ yrsDBDeep Barot
Founder, ContextQA (host)
Where do AI and ML genuinely help software testing today? An experienced QA innovator separates hype from reality — converting manual cases to scripts, self-healing locators, and generating test data with prompting — while being candid about accuracy limits and the non-negotiable role of manual review.
Walk away knowing how to apply it
What the conversation covers
Why AI surged now: compute, training data, and capable models
AI across the QA lifecycle: requirements, automation, regression, impact analysis
Converting manual cases to scripts and auto-building page objects
Self-healing: finding an alternate locator instead of failing the test
Few-shot prompting for domain-specific and performance test data
The accuracy reality: 60–70% is good, improved with training and feedback loops
Real org blockers: cost, PII/security (mitigated by data masking), infrastructure
The ideas worth remembering
AI is assistive — treat outputs as accelerators, then review
~60–70% accuracy is realistic; raise it with training data and feedback
Manual review isn't going anywhere
Just as automation didn't take QA jobs, AI won't either — upskill
It will not take away your job — it is here to help you. You just have to know how to use it.— Anjali
Who you'll hear from
Anjali
QA Innovator, 18+ yrs
Deep Barot
Founder, ContextQA (host)
See ContextQA in action
Go from watching to doing — spin up an AI agent and watch it test, self-heal, and report for you.