ProsaBuddy: Assisting Mechanized Real-Time Schedulability Analysis with LLM-based Agents

Abstract:Rigorous schedulability analysis is essential for the design of hard real-time systems, yet errors in pen-and-paper proofs threaten the safety of critical applications. The Prosa initiative addresses this by offering a foundation for building machine-checkable schedulability analysis proofs in the Rocq proof assistant. However, the substantial time and expertise required to construct such proofs remain a major barrier for wider adoption of Prosa. This work presents ProsaBuddy, an LLM?based agent system designed to lower the effort needed to develop mechanized real-time schedulability proofs. ProsaBuddy employs a ReAct loop with retrieval over the Prosa codebase, access to Rocq tools and optional human-written hints. It uses a subgoal?delegation architecture, decomposing a lemma into subgoals and dispatches them to subagents for proof. We evaluate ProsaBuddy on a mini benchmark drawn from real-time scheduling literature. Experiment results show that ProsaBuddy significantly outper?forms state-of-the-art LLM-based Rocq automated proving agent systems and a general coding agent OpenCode

Logic in Computer Science (cs.LO); Artificial Intelligence (cs.AI)

arXiv:2610.03796 [cs.LO]

(or arXiv:2610.03796v1 [cs.LO] for this version)

https://doi.org/10.48550/arXiv.2610.03796 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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