Towards Leveraging Large Language Model Summaries for Topic Modeling in Source Code (2504.17426v1)

Abstract: Understanding source code is a topic of great interest in the software engineering community, since it can help programmers in various tasks such as software maintenance and reuse. Recent advances in LLMs have demonstrated remarkable program comprehension capabilities, while transformer-based topic modeling techniques offer effective ways to extract semantic information from text. This paper proposes and explores a novel approach that combines these strengths to automatically identify meaningful topics in a corpus of Python programs. Our method consists in applying topic modeling on the descriptions obtained by asking an LLM to summarize the code. To assess the internal consistency of the extracted topics, we compare them against topics inferred from function names alone, and those derived from existing docstrings. Experimental results suggest that leveraging LLM-generated summaries provides interpretable and semantically rich representation of code structure. The promising results suggest that our approach can be fruitfully applied in various software engineering tasks such as automatic documentation and tagging, code search, software reorganization and knowledge discovery in large repositories.

• The paper presents a method integrating LLMs with topic modeling to extract semantic topics from source code lacking traditional documentation.
• The approach generates natural language summaries from obfuscated Python functions using Gemma, then applies BERTopic for topic extraction.
• Validation shows LLM-generated summaries yield topics semantically aligned with original docstrings, outperforming those derived from function names.

Toward Leveraging LLM Summaries for Topic Modeling in Source Code

The paper "Towards Leveraging LLM Summaries for Topic Modeling in Source Code" presents a methodological advancement in the field of source code analysis by integrating LLMs with topic modeling techniques. This interdisciplinary approach aims to automatically extract semantically rich topics from Python code, thus addressing a key challenge in software engineering: understanding source code effectively for tasks such as maintenance and reuse.

Research Context and Approach

Traditionally, topic modeling on source code has relied heavily on natural elements like comments and identifiers introduced by programmers. These elements, while useful, may not always be present in a consistent or meaningful way across different codebases. The proposed method circumvents this limitation by utilizing LLMs to generate natural language summaries from obfuscated Python functions where comments and explicit identifiers have been removed. This LLM-generated text is then subjected to topic modeling to infer high-level topics corresponding to the original source code.

The researchers employed a combination of methods to achieve their goal. They initially preprocessed the code to eliminate natural-language cues, leveraged the Gemma model to produce text summaries, and applied BERTopic—a sophisticated topic modeling algorithm that uses transformer embeddings and clustering techniques. This approach allows for extracting latent semantic information in a manner that mimics the presence of human-readable documentation, albeit derived from the structural and syntactic elements inherent in the code itself.

Experimental Design and Validation

To validate their method, the authors compared the topics derived from LLM-generated summaries against those obtained from function names and existing docstrings across a dataset of 10,000 Python functions. Four evaluation metrics were deployed, including mean squared error of topic distributions and cosine similarity measures, to quantify the distance and coherence between topics across different modeling contexts.

The findings demonstrated that the topics arising from LLM-generated summaries show significant semantic alignment with those derived from the original docstrings, reflecting the internal consistency and richness of the topics generated. Notably, when compared to topics generated from function names alone, the method based on LLM summaries was superior in maintaining semantic integrity, suggesting that LLM-generated text can effectively substitute for traditional documentation under certain conditions.

Implications and Future Directions

The implications of this research are substantive for the domain of source code analysis. The method provides a robust alternative for source code bases that lack properly maintained documentation, enabling applications in code search, automatic documentation, and reorganization of software repositories. From a theoretical standpoint, this paper reinforces the potential of LLMs in comprehending code semantics and contributing to program analysis tasks.

Looking forward, the integration of LLMs in source code topic modeling could evolve with more sophisticated LLMs and embeddings, potentially extending to other programming languages and encompassing broader software engineering workflows. The exploration of combining other machine learning techniques with topic modeling to further enhance semantic extraction from code structures is another promising direction.

Overall, this paper underscores the capability of leveraging advances in natural language processing to tackle longstanding challenges in software engineering, offering a new lens through which to view and interact with large, undocumented codebases efficiently.