Large Language Models (LLMs) for Source Code Analysis: applications, models and datasets

Hamed Jelodar; Mohammad Meymani; Roozbeh Razavi-Far

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arXiv · 2503.17502

Large Language Models (LLMs) for Source Code Analysis: applications, models and datasets

Hamed Jelodar·Mohammad Meymani·Roozbeh Razavi-Far

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Abstract

Large language models (LLMs) and transformer-based architectures are increasingly utilized for source code analysis. As software systems grow in complexity, integrating LLMs into code analysis workflows becomes essential for enhancing efficiency, accuracy, and automation. This paper explores the role of LLMs for different code analysis tasks, focusing on three key aspects: 1) what they can analyze and their applications, 2) what models are used and 3) what datasets are used, and the challenges they face. Regarding the goal of this research, we investigate scholarly articles that explore the use of LLMs for source code analysis to uncover research developments, current trends, and the intellectual structure of this emerging field. Additionally, we summarize limitations and highlight essential tools, datasets, and key challenges, which could be valuable for future work.

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Hamed Jelodar, Mohammad Meymani, Roozbeh Razavi-Far. 2025-03-21. Large Language Models (LLMs) for Source Code Analysis: applications, models and datasets. https://arxiv.org/abs/2503.17502

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