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This research introduces Monty, a framework that significantly improves the reliability of translating natural language into executable formal assertions using LLMs. By employing novel conformance scoring metrics rather than naive translation methods, it achieves up to 20 points higher precision in generating ground truth specifications for software verification tasks. The work addresses critical challenges in bridging informal developer intent with rigorous mathematical proofs within automated testing pipelines.
Faithful Autoformalization of Natural Language Assertions
arXiv:2607.13303v2 Announce Type: replace-cross Abstract: Formal contracts are essential for software testing and verification, yet writing them remains labor-intensive and error-prone. LLMs offer a promising path toward autoformalization: synthesizing executable assertions from natural-language specifications and thereby bridging the gap between informal developer intent and formal executable specifications. We present Monty: an autoformalization framework for assertions that tackles the challenges of expectations of validity of assertions and ambiguity in natural-language. Our techniques are based on filtering formalizations using a novel conformance score metric and validity scores obtained from testing the code against formalized assertions. We evaluate our approach on 541 assertion-generation tasks derived from 22 collection-like Java classes, and show that our technique produces the ground truth more reliably (improving upto 20 points in precision on average) than when using LLMs naively to translate assertions.
Abstract:Formal contracts are essential for software testing and verification, yet writing them remains labor-intensive and error-prone. LLMs offer a promising path toward autoformalization: synthesizing executable assertions from natural-language specifications and thereby bridging the gap between informal developer intent and formal executable specifications. We present Monty: an autoformalization framework for assertions that tackles the challenges of expectations of validity of assertions and ambiguity in natural-language. Our techniques are based on filtering formalizations using a novel conformance score metric and validity scores obtained from testing the code against formalized assertions. We evaluate our approach on 541 assertion-generation tasks derived from 22 collection-like Java classes, and show that our technique produces the ground truth more reliably (improving upto 20 points in precision on average) than when using LLMs naively to translate assertions.
Software Engineering (cs.SE); Artificial Intelligence (cs.AI)
arXiv:2607.13303 [cs.SE]
(or arXiv:2607.13303v2 [cs.SE] for this version)
https://doi.org/10.48550/arXiv.2607.13303 Focus to learn more arXiv-issued DOI via DataCite
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