Kajal: Extracting Grammar of a Source Code Using Large Language Models

Autor:	Torkamani, Mohammad Jalili
Rok vydání:	2024
Předmět:	Computer Science - Software Engineering Computer Science - Artificial Intelligence D.2 D.3 F.4.2 I.2.5
Druh dokumentu:	Working Paper
Popis:	Understanding and extracting the grammar of a domain-specific language (DSL) is crucial for various software engineering tasks; however, manually creating these grammars is time-intensive and error-prone. This paper presents Kajal, a novel approach that automatically infers grammar from DSL code snippets by leveraging Large Language Models (LLMs) through prompt engineering and few-shot learning. Kajal dynamically constructs input prompts, using contextual information to guide the LLM in generating the corresponding grammars, which are iteratively refined through a feedback-driven approach. Our experiments show that Kajal achieves 60% accuracy with few-shot learning and 45% without it, demonstrating the significant impact of few-shot learning on the tool's effectiveness. This approach offers a promising solution for automating DSL grammar extraction, and future work will explore using smaller, open-source LLMs and testing on larger datasets to further validate Kajal's performance. Comment: 9 pages, 6 figures, 1 table, preprint
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2412.08842 Zobrazit plný text záznamu View this record from Arxiv