verify-work

Conversational testing validates software features by guiding you through interactive scenarios, asking if reality matches expectations, and capturing deviations as issues with inferred severity. It creates a UAT.md file to track progress and feeds identified gaps into a planning phase.

Tracking UAT progress across context resets requires persistent state management. This approach creates UAT.md files to log test progress, ensuring your validation sessions survive interruptions and maintain continuity without losing tracked issues.

Automated issue capture during user acceptance testing logs discrepancies between expected and actual outcomes. It presents expected results, captures your feedback, and automatically records bugs with inferred severity to streamline gap analysis.

Conversational AI can document bug tracking gaps by validating built features interactively and feeding identified issues into a planning phase. This automates resolution tracking by capturing deviations and inferring severity for downstream planning.

Persistent state management for feature validation is needed when testing spans multiple sessions or faces context resets. It ensures your UAT.md progress files survive interruptions, maintaining test continuity and tracking gaps without data loss.