Each phase runs short iterations where all disciplines work together, not in sequence. Phases overlap throughout the project lifecycle.
One diagram showing how the four phases interlock, from initial requirements through to production. Colors mark the phases: blue for Inception, green for Elaboration, red for Construction, orange for Transition.
The same methodology adapts to two realities, a clean slate, or an existing system you can't break.
The AI drafts use cases and an entity model from the requirements; the Requirements Engineer revises and owns them. The AI agent then generates code and tests directly from those artifacts. The Software Engineer reviews the result — every artifact traces back to a requirement.
Start from the running system. The Software Engineer reverse-engineers the entity model, use case model, and specifications from the existing code. Software Engineer and Requirements Engineer then review those artifacts together, establishing the spec baseline that future iterations build on.
Principles that ensure success in agile, iterative development.
Use cases are owned by requirements engineering and maintained for the life of the system — not prompts a developer writes and discards.
AI handles tedious work; humans focus on business logic.
Specs, code, and tests evolve together through short cycles.
Comprehensive tests ensure consistent behavior during AI regeneration.
Continuous validation with business users at every iteration.
Every line of code traces back to a business requirement.
It's not about perfect specs, it's about iterative improvement.
Critics argue AI code generation only works with exhaustive specifications that force deterministic output. This assumes we need perfect requirements upfront.
Reality: Perfect specifications are impossible and unnecessary. The real value comes from iterative improvement.
Through short cycles, specifications become clearer, AI generation improves, and tests get stronger. Each iteration builds on the previous one.
Key insight: Tests ensure consistent behavior regardless of how the AI generates code. This enables safe evolution and modernization.
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