On the Effectiveness of Large Language Models in Writing Alloy Formulas

[2502.15441] On the Effectiveness of Large Language Models in Writing Alloy Formulas Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Software Engineering arXiv:2502.15441 (cs) [Submitted on 21 Feb 2025] Title: On the Effectiveness of Large Language Models in Writing Alloy Formulas Authors: Yang Hong , Shan Jiang , Yulei Fu , Sarfraz Khurshid View a PDF of the paper titled On the Effectiveness of Large Language Models in Writing Alloy Formulas, by Yang Hong and 3 other authors View PDF HTML (experimental) Abstract: Declarative specifications have a vital role to play in developing safe and dependable software systems. Writing specifications correctly, however, remains particularly challenging. This paper presents a controlled experiment on using large language models (LLMs) to write declarative formulas in the well-known language Alloy. Our use of LLMs is three-fold. One, we employ LLMs to write complete Alloy formulas from given natural language descriptions (in English). Two, we employ LLMs to create alternative but equivalent formulas in Alloy with respect to given Alloy formulas. Three, we employ LLMs to complete sketches of Alloy formulas and populate the holes in the sketches by synthesizing Alloy expressions and operators so that the completed formulas accurately represent the desired properties (that are given in natural language). We conduct the experimental evaluation using 11 well-studied subject specifications and employ two popular LLMs, namely ChatGPT and DeepSeek. The experimental results show that the LLMs generally perform well in synthesizing complete Alloy formulas from input properties given in natural language or in Alloy, and are able to enumerate multiple unique solutions. Moreover, the LLMs are also successful at completing given sketches of Alloy formulas with respect to natural language descriptions of desired properties (without requiring test cases). We believe LLMs offer a very exciting advance in our ability to write specifications, and can help make specifications take a pivotal role in software development and enhance our ability to build robust software. Subjects: Software Engineering (cs.SE) ; Artificial Intelligence (cs.AI); Formal Languages and Automata Theory (cs.FL); Programming Languages (cs.PL) Cite as: arXiv:2502.15441 [cs.SE] (or arXiv:2502.15441v1 [cs.SE] for this version) https://doi.org/10.48550/arXiv.2502.15441 Focus to learn more arXiv-issued DOI via DataCite Related DOI : https://doi.org/10.1145/3822455.3830309 Focus to learn more DOI(s) linking to related resources Submission history From: Shan Jiang [ view email ] [v1] Fri, 21 Feb 2025 13:09:58 UTC (122 KB) Full-text links: Access Paper: View a PDF of the paper titled On the Effectiveness of Large Language Models in Writing Alloy Formulas, by Yang Hong and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.SE < prev | next > new | recent | 2025-02 Change to browse by: cs cs.AI cs.FL cs.PL References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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