AI made coding faster, but your releases didn’t speed up, because every feature still gets tested by hand. I help teams replace that with acceptance tests that run automatically. I have a few spots left for 2027. Book a call.
Most systems are legacy: years in production, no acceptance tests, and nobody can say exactly how they behave. Before every release, someone works through the manual test procedures by hand, and the list grows with every release.
Writing acceptance tests for the whole system by hand would take months, so it never starts. But the behaviour already exists: in the running system, the source code, the manual test procedures and the domain experts’ heads. AI can write it down, and the running system checks what the AI wrote.
The steps:
Phase 1. Prove it on the core use case
Step 1.1. Generate the use case diagram.
Step 1.2. Generate the use case narrative for the core use case.
Step 1.3. Generate the acceptance criteria for the core use case.
Step 1.4. Set up the acceptance test architecture, with the acceptance tests for the core use case.
Step 1.5. Ensure that the acceptance tests pass against the existing system.
Phase 2. Scale to the other use cases
Step 2.1. Generate the use case narratives for the other use cases.
Step 2.2. Generate the acceptance criteria for the other use cases.
Step 2.3. Generate the acceptance tests for the other use cases.
Step 2.4. Ensure that the acceptance tests pass against the existing system.
Step 2.5. Delete the acceptance criteria.
Take one use case all the way through before doing the rest. Each step comes with its prompt. The examples come from an online shop.
