Kajal: Extracting Grammar of a Source Code Using Large Language Models
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866913609292972032 |
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| author | Torkamani, Mohammad Jalili |
| author_facet | Torkamani, Mohammad Jalili |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_08842 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Kajal: Extracting Grammar of a Source Code Using Large Language Models Torkamani, Mohammad Jalili Software Engineering Artificial Intelligence D.2; D.3; F.4.2; I.2.5 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. |
| title | Kajal: Extracting Grammar of a Source Code Using Large Language Models |
| topic | Software Engineering Artificial Intelligence D.2; D.3; F.4.2; I.2.5 |
| url | https://arxiv.org/abs/2412.08842 |