Not All Invariants Are Equal: Curating Training Data to Accelerate Program Verification with SLMs
arXiv:2603.15510v2 Announce Type: replace Abstract: The synthesis of inductive loop invariants remains a critical bottleneck in automated program verification. While Large Language Models (LLMs) show promise in mitigating this issue, they often fail on complex programs, producing invariants that are invalid or computationally ineffective. Although fine-tuning is a natural strategy to address these limitations, obtaining high-quality training data remains an open challenge. We first formalize the properties required for a high-quality training invariant, and then present Wonda, a rigorous data
The increasing complexity of software and the reliance on automated verification methods are pushing the boundaries of current AI capabilities, making improved invariant synthesis crucial.
This research addresses a bottleneck in automated program verification, which can significantly enhance the reliability and security of critical software systems, impacting various industries and national infrastructure.
The ability to generate high-quality training data for invariant synthesis could lead to more robust and accelerated program verification, improving software development cycles and trustworthiness.
- · Software developers
- · Cybersecurity sector
- · AI model developers
- · Automated verification tools
- · Manual verification processes
- · Systems unconcerned with formal verification
More reliable and secure software applications become commonplace due to enhanced verification tools.
The cost and time associated with software debugging and vulnerability patching decrease substantially.
Increased trust in autonomous systems and critical infrastructure software, potentially accelerating their deployment in sensitive areas.
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Read at arXiv cs.LG