Finding Inductive Loop Invariants using Large Language Models

no code implementations • 14 Nov 2023 • Adharsh Kamath, Aditya Senthilnathan, Saikat Chakraborty, Pantazis Deligiannis, Shuvendu K. Lahiri, Akash Lal, Aseem Rastogi, Subhajit Roy, Rahul Sharma

Finally, we explore the effectiveness of using an efficient combination of a symbolic tool and an LLM on our dataset and compare it against a purely symbolic baseline.

Ranking LLM-Generated Loop Invariants for Program Verification

1 code implementation • 13 Oct 2023 • Saikat Chakraborty, Shuvendu K. Lahiri, Sarah Fakhoury, Madanlal Musuvathi, Akash Lal, Aseem Rastogi, Aditya Senthilnathan, Rahul Sharma, Nikhil Swamy

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.

Re-Ranking