Code-centric development leads to maintenance problems, hinders modernization, and causes business misalignment.
Traditional development is code-centric. Requirements get outdated, documentation drifts, and when bugs appear, we dig through code to understand what the system was supposed to do.
AI coding tools make this worse by generating code faster without fixing the underlying process problems.
AI Unified Process flips this around. Requirements stay at the center, and everything else gets generated from them using AI as the consistency engine.
Iterative Improvement: Through short iterations, specifications, code, and tests improve together. Documentation enables sustainable development and modernization.
Test-Driven Consistency: Tests ensure the system behaves the same regardless of code generation changes, enabling safe refactoring and evolution.
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.
The same methodology adapts to two realities, a clean slate, or an existing system you can't break.
A Requirements Engineer creates use cases and an entity model. The AI agent generates code and tests directly from those artifacts. The Software Engineer reviews, 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.
Most spec-driven development initiatives stop at the prototype. AIUP is battle-tested in enterprise environments and adapted for AI-native workflows.
Every line of code traces back to a business requirement. Audit-ready by design, for compliance reviews, security audits, and regulatory frameworks.
The Brownfield workflow reverse-engineers specifications from running code, so you can modernize the mission-critical systems you can't simply replace.
Requirements Engineers, Software Engineers, and business stakeholders work in parallel across iterations. Clear roles, shared artifacts, no single bottleneck.
Test-protected regeneration ensures AI improvements never silently change behavior. Upgrade models, swap tools, refactor freely, without fear of regression.
Living specifications survive team changes, onboarding, and reorganizations. The system documents itself, no tribal knowledge, no key-person risk.
Watch how AIUP transforms development workflows.
Principles that ensure success in agile, iterative development.
Specifications drive everything else, not code.
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.
Measurable improvements across every aspect of software delivery.
Stakeholders review every artifact, ensuring the system matches actual needs.
Living documentation enables refactoring and modernization without losing knowledge.
Tests protect system behavior while AI improves code generation quality.
Specifications, code, and tests improve together through continuous cycles.
From business requirement to code line, every connection is maintained.
AIUP ships with ready-to-use plugins for Claude Code that automate the entire workflow, from requirements to implementation to testing.
The aiup-core plugin provides slash commands that guide Claude through creating requirements catalogs, entity models, use case diagrams, and specifications, all stack-agnostic.
/requirements
/entity-model
/use-case-diagram
/use-case-spec
The aiup-vaadin-jooq plugin adds technology-specific skills for database migrations, UI implementation with Vaadin, and automated testing with Vaadin Browserless and Playwright.
/flyway-migration
/implement
/browserless-test
/playwright-test
The AI Unified Process Navigator is a standalone IntelliJ plugin that links @UseCase annotated test methods to their UC-XXX-*.md specifications via gutter icons, and back from the spec to the test.
@UseCase
UC-XXX-*.md
AIUP combines the best of Rational Unified Process with modern AI tooling.