
Codex
AI
Commercial
<What you get/>
A fast map of what an inherited suite actually covers, verified by execution
Mechanical migration and refactor work done in a fraction of the manual time
Test logic translated between languages when consolidating stacks
Engineer review before any generated change reaches your repository
What it is
Codex is OpenAI's code-generation model, used to draft automation code from a described intent and to refactor existing suites. As with any generated test code the value depends on review: a generated test that passes for the wrong reason is worse than no test, so output is treated as a first draft rather than a result.
How PerfectQA uses Codex
We use Codex for code-level reasoning tasks that sit above autocomplete: reading an unfamiliar test suite and explaining what it actually covers, drafting a refactor across many similar files, and translating test logic between languages when a client is consolidating stacks. On inherited automation estates — where we often arrive with thousands of tests and no documentation — this is genuinely useful for building a map fast, though we verify the map by running the suite rather than trusting the summary. It is also effective at the mechanical part of migrations, such as converting a large body of tests from one assertion library to another, where the transformation is consistent and reviewable. As with all of these, output is treated as a draft: an engineer reviews before anything reaches a client repository, and we do not point it at production systems or client-confidential data.
Category
AI
In our stack
2 years
Projects
5 delivered
Frequent questions
Can it understand our existing test suite?
Is it safe to use on our repository?
Can it migrate our tests to a new framework?
How do you stop it inventing things?
Every stack is different
Tell us yours, and we’ll show you where this fits


