Requirements analysis: catching requirement bugs before they become code

(this is a technical blog with research references. Yes, it's also a pitch for Kiro, which is an AWS product. No, I'm not affiliated.)

Conclusion:

Requirement bugs are expensive. They propagate through detailed design, into task planning, and finally into code, are hard to detect on first read, and costly to fix. AI-assisted coding makes the stakes higher, because code being generated from vague specifications can land in production faster than any human can review it for these classes of subtle bugs.

Requirements analysis helps raise the quality of your specifications in three steps using a neuro-symbolic approach: First, refinement uses LLM-driven reasoning to turn thesis-level requirements into more testable, solution-free, and more consistent and complete requirements. Second, auto-formalization produces a formal model that represents the natural language faithfully, detecting semantic ambiguity on the fly and asking for clarification where needed. Third, logical analysis uses an automated reasoning engine to detect consistency and completeness problems on the formal model, and generate scenarios exemplifying accepted and rejected system behaviors. Each finding is surfaced as a concise question with two concrete answer options. Once you've answered the questions, an LLM rewrites the affected requirements to reflect your choices explicitly, producing an updated specification that is better aligned with your intent, ready for detailed design and implementation.