An Empirical Evaluation of Pre-trained Large Language Models for Repairing Declarative Formal Specifications

• URL: http://arxiv.org/abs/2404.11050v1
• Date: Wed, 17 Apr 2024 03:46:38 GMT
• Title: An Empirical Evaluation of Pre-trained Large Language Models for Repairing Declarative Formal Specifications
• Authors: Mohannad Alhanahnah, Md Rashedul Hasan, Hamid Bagheri,
• Abstract summary: This paper presents a systematic investigation into the capacity of Large Language Models (LLMs) for repairing declarative specifications in Alloy. We propose a novel repair pipeline that integrates a dual-agent LLM framework, comprising a Repair Agent and a Prompt Agent. Our study reveals that LLMs, particularly GPT-4 variants, outperform existing techniques in terms of repair efficacy, albeit with a marginal increase in runtime and token usage.
• Score: 5.395614997568524
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automatic Program Repair (APR) has garnered significant attention as a practical research domain focused on automatically fixing bugs in programs. While existing APR techniques primarily target imperative programming languages like C and Java, there is a growing need for effective solutions applicable to declarative software specification languages. This paper presents a systematic investigation into the capacity of Large Language Models (LLMs) for repairing declarative specifications in Alloy, a declarative formal language used for software specification. We propose a novel repair pipeline that integrates a dual-agent LLM framework, comprising a Repair Agent and a Prompt Agent. Through extensive empirical evaluation, we compare the effectiveness of LLM-based repair with state-of-the-art Alloy APR techniques on a comprehensive set of benchmarks. Our study reveals that LLMs, particularly GPT-4 variants, outperform existing techniques in terms of repair efficacy, albeit with a marginal increase in runtime and token usage. This research contributes to advancing the field of automatic repair for declarative specifications and highlights the promising potential of LLMs in this domain.

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