{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/towards-ai-assisted-synthesis-of-verified","title":"Towards AI-Assisted Synthesis of Verified Dafny Methods","arxiv_id":"2402.00247","date":"2024-02-01","proceeding":null,"authors":["Md Rakib Hossain Misu","Cristina V. Lopes","Iris Ma","James Noble"],"abstract":"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...","url_abs":"https://arxiv.org/abs/2402.00247v2","url_pdf":"https://arxiv.org/pdf/2402.00247v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"towards-ai-assisted-synthesis-of-verified","repo_url":"https://github.com/mondego/dafny-synthesis","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2402.00247","atlas_url":"https://app.syntology.ai/?focus=2402.00247","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}