Towards AI-Assisted Synthesis of Verified Dafny Methods
| Autor: | Misu, Md Rakib Hossain, Lopes, Cristina V., Ma, Iris, Noble, James |
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| Rok vydání: | 2024 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1145/3643763 |
| Popis: | Large language models show great promise in many domains, including programming. A promise is easy to make but hard to keep, and language models often fail to keep their promises, generating erroneous code. A promising avenue to keep models honest is to incorporate formal verification: generating programs' specifications as well as code so that the code can be proved correct with respect to the specifications. Unfortunately, existing large language models show a severe lack of proficiency in verified programming. In this paper, we demonstrate how to improve two pretrained models' proficiency in the Dafny verification-aware language. Using 178 problems from the MBPP dataset, we prompt two contemporary models (GPT-4 and PaLM-2) to synthesize Dafny methods. We use three different types of prompts: a direct Contextless prompt; a Signature prompt that includes a method signature and test cases, and a Chain of Thought (CoT) prompt that decomposes the problem into steps and includes retrieval augmentation generated example problems and solutions. Our results show that GPT-4 performs better than PaLM-2 on these tasks and that both models perform best with the retrieval augmentation generated CoT prompt. GPT-4 was able to generate verified, human-evaluated, Dafny methods for 58% of the problems, however, GPT-4 managed only 19% of the problems with the Contextless prompt, and even fewer (10%) for the Signature prompt. We are thus able to contribute 153 verified Dafny solutions to MBPP problems, 50 that we wrote manually, and 103 synthesized by GPT-4. Our results demonstrate that the benefits of formal program verification are now within reach of code generating large language models... Comment: This is an author provided preprint. The final version will be published at Proc. ACM Softw. Eng; FSE 2024, in July 2024 |
| Databáze: | arXiv |
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