In many Java projects, specifications and code drift apart over time. Requirements change, documentation becomes outdated, and developers rely mainly on the code. This increases risk and makes changes harder, especially in long-living business applications.

This talk presents a spec-driven approach where system use cases are the central artifact. A system use case describes observable system behavior and acts as a stable contract for the application. Code is derived from these use cases instead of treating the code itself as the source of truth.

AI is used as a supporting tool to generate and update code and tests from system use cases in small, controlled steps. The focus is not on full regeneration, but on keeping existing code and specifications aligned over time.
Using concrete examples, the talk shows how backend logic, database access, and UI behavior can evolve together. It also explains when code is generated, when it is updated, and how version control and reviews help keep changes small and understandable.

The session shares concrete workflows and lessons learned from daily Java development, including limitations and trade-offs of using AI in this way.