AI enabled QA with TestChimp

AI has made building products (a.k.a inner loop of SDLC) dramatically faster. Yet, most teams still rely on manual or ad-hoc solutions when it comes to “ensuring the product actually works as intended” (a.k.a outer loop of SDLC). This makes testing and verification the new bottleneck of software development.

However, for effective execution of the outer-loop, 2 contexts need to be brought in with test traceability:

• Product Context: The intended behaviour (as described through user stories / scenarios / knowledge-base)
• Production Context: The real user behaviour – the user segments observed, user journeys executed in production, variations of journeys observed etc.

Those contexts today live in silo’ed tools built in pre-LLM era (making them inaccessible to agents), without test traceability. This makes it harder to execute the outer-loop with AI agents.

TestChimp makes those 2 contexts agent accessible – with test traceability, so that agents can identify gaps in testing, and execute the outer loop of your SDLC effectively.

In this course, you will learn

• the core principles of making those contexts agent accessible,
• how test traceability gets implemented,
• what capabilities gets unlocked by bringing in those contexts to inform testing
• how to use agents to cover non-functional and functional testing – to ensure the deployed software are production ready