Auto-Generating E2E Test-Cases and Mocks close to Reality w/ KeployCheck Reference
Since testing is very use-case specific, developers often avoid spending effort in writing test cases. Manual effort is being spent by QA to test apps and the industry standard for test automation is 24%. On average 50% of engineering efforts are spent to write and maintain the test scripts. Creating dummy test data is also very time-consuming and still, it is unrealistic test -data, leaving bugs leaking to production.
The new-gen AI LLM-based test generation tools like ChatGPT are not fire-and-forget, since it requires effort to understand and correct the scripts generated by those tools and the dummy data is again unrealistic.
In this session we're going to talk about how we can easily record the API calls from the user-traffic and convert those to realistic test cases and data mocks/stubs without writing any scripts. How we can set our testing pipelines on auto-pilot? It's like having our own TestGPT, to save time and streamline our testing process.
We'll also talk about how we can integrate this pipeline in popular language native testing libraries like JUnit, Jest, Go-Test and easily achieve high test coverage on functional test suites.