Shuvendu Lahiri


2023

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Ranking LLM-Generated Loop Invariants for Program Verification
Saikat Chakraborty | Shuvendu Lahiri | Sarah Fakhoury | Akash Lal | Madanlal Musuvathi | Aseem Rastogi | Aditya Senthilnathan | Rahul Sharma | Nikhil Swamy
Findings of the Association for Computational Linguistics: EMNLP 2023

Synthesizing inductive loop invariants is fundamental to automating program verification. In this work we observe that Large Language Models (such as gpt-3.5 or gpt-4) are capable of synthesizing loop invariants for a class of programs in a 0-shot setting, yet require several samples to generate the correct invariants. This can lead to a large number a calls to a program verifier to establish an invariant. To address this issue, we propose a re-ranking approach for the generated results of LLMs. We have designed a ranker that can distinguish between correct inductive invariants and incorrect attempts based on the problem definition. The ranker is optimized as a contrastive ranker. Experimental results demonstrate that this re-ranking mechanism significantly improves the ranking of correct invariants among the generated candidates, leading to a notable reduction in the number of calls to a verifier.