We Open-Sourced Our AI Testing Platform: From Requirement Docs to Executable Test Cases

Every tester knows the drill: a requirement document lands, and you spend the next few days manually translating it into test case tables — preconditions, steps, expected results. Then the API/UI automation scripts you wrote last quarter break because one page changed.

We got tired of it, so we built WHartTest and open-sourced it: an AI-driven test automation platform that turns requirement documents into executable test cases — and helps fix them when they fail.

Functional test case generation. Upload requirement documents (with an online editor built in). AI generates complete cases — preconditions, steps, expected results — grounded in your project knowledge base. Cases are managed in list or mindmap view, with drag & drop, P0–P3 tags, and export to PNG/JSON.

API automation. From an API definition, AI generates request steps, assertions, variable extraction, and pre/post scripts. When a run fails, AI reads the logs and responses to locate the problem and proposes fixes.

UI automation. AI generates page operation steps and element locators. Runs capture logs, screenshots, video, and Playwright traces. When a locator breaks or an assertion fails, AI analyzes the evidence and suggests repairs. Since v2.8.0 there is a frame-stream Playwright recorder: you record interactively and create elements/steps as you go — no more hand-writing scripts.

Other recent highlights: smart dual-mode execution (script / pure-AI / dual), AI self-healing failure diagnosis, multi-format API doc import & export (OpenAPI, Swagger, Postman, HAR, curl, Apifox, YApi...), and operation audit logs.

We'd love feedback from QA engineers: how is AI-generated test case generation working out in your teams? What would you want next?