This paper introduces a framework for independently verifying software produced by AI coding agents. The authors argue that when an AI system generates both the software and its tests, the tests lack independent assurance and can become an optimization target. They propose a system that separates the building and verifying processes, establishing a "Verification Contract" to define required demonstrations before release. This framework includes a specification-first verification phase and an implementation-aware phase using mutation analysis, property-based testing, and fuzzing, ultimately producing an assurance report for human review. AI
IMPACT This research addresses a critical gap in AI-generated software, proposing methods to ensure reliability and trustworthiness beyond self-generated tests.
RANK_REASON Academic paper proposing a new framework for software verification. [lever_c_demoted from research: ic=1 ai=1.0]
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