ChatGPT Writes Tests, But ContextQA Understands Them
Founder & CEO, ContextQANKNaveen Khunteta
Host, Naveen AutomationLabs
Generating test code is now commoditized — so what makes testing actually work? In this live session with Naveen AutomationLabs, ContextQA founder Deep Barot shows why context-driven testing beats raw AI-generated scripts, with live MCP demos that turn a single prompt into test suites, impact analysis, and root cause analysis.
Walk away knowing how to apply it
What the conversation covers
Context-driven testing vs one-off ChatGPT/Claude test generation
The five context sources: product, design, developers, analytics, and SRE/DevOps
ContextQA as end-to-end for web, mobile, API, database, and AI-agent testing
Model Context Protocol (MCP) as the bridge between your tools and the LLM
Live demo: one prompt pulls a Jira epic and builds test cases, a suite, and a plan
Live demo: impact analysis scoring risk and affected regression cases before merge
Live demo: root cause analysis with evidence, fixes, and a custom quality report
Accuracy as a progression: 70–80% on day one rising as context accumulates
The ideas worth remembering
Generating code is commoditized — it's not a substitute for context-driven testing
Testing is about understanding the domain and the application, not just automation
The human (or agent) must stay in the loop to hold context
Engineers must become auditors with a product-builder mindset
Generating code is commoditized right now. That's why we're taking a different approach — not just script generation, but actual context-driven testing.— Deep Barot
Who you'll hear from
Deep Barot
Founder & CEO, ContextQA
Naveen Khunteta
Host, Naveen AutomationLabs
See ContextQA in action
Go from watching to doing — spin up an AI agent and watch it test, self-heal, and report for you.