Semantic Source Code Segmentation using Small and Large Language Models

• URL: http://arxiv.org/abs/2507.08992v1
• Date: Fri, 11 Jul 2025 19:49:59 GMT
• Title: Semantic Source Code Segmentation using Small and Large Language Models
• Authors: Abdelhalim Dahou, Ansgar Scherp, Sebastian Kurten, Brigitte Mathiak, Madhu Chauhan,
• Abstract summary: This paper introduces an automated, domain-specific approach for research R code segmentation using Large and Small Language Models (LLMs/SLMs)<n>We explore two distinct approaches: line-by-line analysis with context and range-based segment determination.<n>Our results show that context-based line-by-line analysis is superior over range-based segmentation.
• Score: 2.5748316361772963
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Source code segmentation, dividing code into functionally coherent segments, is crucial for knowledge retrieval and maintenance in software development. While enabling efficient navigation and comprehension of large codebases, manual and syntactic analysis approaches have become impractical as repositories grow, especially for low-resource languages like R and their research domains (e.g., social sciences, psychology).This paper introduces an automated, domain-specific approach for research R code segmentation using Large and Small Language Models (LLMs/SLMs). It presents two novel approaches and a human-annotated dataset, StatCodeSeg. We explore two distinct approaches: line-by-line analysis with context and range-based segment determination. We experiment with LLMs and fine-tuned SLMs. To support the generalizability of our approaches, we also include experiments on Python code from the computer science domain.Our results show that context-based line-by-line analysis is superior over range-based segmentation.Using smaller language models like CodeBERT and an encoder-only version of CodeT5+ are better than their LLM counterparts. Most notably, these two best-performing models did not see R code during pre-training versus the LLMs but were only fine-tuned on 4,130 lines of manually annotated code.

Related papers

• Segment First or Comprehend First? Explore the Limit of Unsupervised Word Segmentation with Large Language Models [92.92512796044471]
  We propose a new framework to explore the limit of unsupervised word segmentation with Large Language Models (LLMs)<n>We employ current mainstream LLMs to perform word segmentation across multiple languages to assess LLMs' "comprehension"<n>We introduce a novel unsupervised method, termed LLACA, which enables the construction of a dynamic $n$-gram model that adjusts based on contextual information.
  arXiv  Detail & Related papers  (2025-05-26T07:48:15Z)
• Is Compression Really Linear with Code Intelligence? [60.123628177110206]
  textitFormat Annealing is a lightweight, transparent training methodology designed to assess the intrinsic capabilities of pre-trained models equitably.<n>Our empirical results reveal a fundamental logarithmic relationship between measured code intelligence and bits-per-character (BPC)<n>Our work provides a more nuanced understanding of compression's role in developing code intelligence and contributes a robust evaluation framework in the code domain.
  arXiv  Detail & Related papers  (2025-05-16T16:59:14Z)
• OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models [76.59316249991657]
  Large language models (LLMs) for code have become indispensable in various domains, including code generation, reasoning tasks and agent systems.<n>While open-access code LLMs are increasingly approaching the performance levels of proprietary models, high-quality code LLMs remain limited.<n>We introduce OpenCoder, a top-tier code LLM that not only achieves performance comparable to leading models but also serves as an "open cookbook" for the research community.
  arXiv  Detail & Related papers  (2024-11-07T17:47:25Z)
• Crystal: Illuminating LLM Abilities on Language and Code [58.5467653736537]
  We propose a pretraining strategy to enhance the integration of natural language and coding capabilities. The resulting model, Crystal, demonstrates remarkable capabilities in both domains.
  arXiv  Detail & Related papers  (2024-11-06T10:28:46Z)
• Bug In the Code Stack: Can LLMs Find Bugs in Large Python Code Stacks [1.3586572110652484]
  This study explores the capabilities of Large Language Models (LLMs) in retrieving contextual information from large text documents. Our benchmark, Bug In The Code Stack (BICS), is designed to assess the ability of LLMs to identify simple syntax bugs within large source code. Our findings reveal three key insights: (1) code-based environments pose significantly more challenge compared to text-based environments for retrieval tasks, (2) there is a substantial performance disparity among different models, and (3) there is a notable correlation between longer context lengths and performance degradation.
  arXiv  Detail & Related papers  (2024-06-21T17:37:10Z)
• Natural Is The Best: Model-Agnostic Code Simplification for Pre-trained Large Language Models [6.646510073473929]
  We propose SlimCode, a model-agnostic code simplification solution for Large Language Models. SlimCode can improve the state-of-the-art technique by 9.46% and 5.15% in terms of MRR and BLEU score on code search and summarization.
  arXiv  Detail & Related papers  (2024-05-18T06:15:52Z)
• LLM-Seg: Bridging Image Segmentation and Large Language Model Reasoning [8.379286663107845]
  Reasoning segmentation is a novel task that enables segmentation system to reason and interpret implicit user intention. Our work on reasoning segmentation contributes on both the methodological design and dataset labeling.
  arXiv  Detail & Related papers  (2024-04-12T18:45:51Z)
• Perplexed: Understanding When Large Language Models are Confused [3.4208414448496027]
  This paper introduces perplexed, a library for exploring where a language model is perplexed. We conducted a case study focused on Large Language Models (LLMs) for code generation using an additional tool we built to help with the analysis of code models called codetokenizer. We found that our studied code LLMs had their worst performance on coding structures where the code was not syntactically correct.
  arXiv  Detail & Related papers  (2024-04-09T22:03:39Z)
• Novel Preprocessing Technique for Data Embedding in Engineering Code Generation Using Large Language Model [7.74830226656449]
  We present four main contributions to enhance the performance of Large Language Models (LLMs) in generating domain-specific code. We introduce the Chain of Density for Renovation Credibility (CoDRC), driven by LLMs, and the Adaptive Text Renovation (ATR) algorithm for assessing data reliability. We also develop the Implicit Knowledge Expansion and Contemplation (IKEC) Prompt technique.
  arXiv  Detail & Related papers  (2023-11-27T19:17:39Z)
• Exploring Large Language Models for Code Explanation [3.2570216147409514]
  Large Language Models (LLMs) have made remarkable strides in Natural Language Processing. This study specifically delves into the task of generating natural-language summaries for code snippets, using various LLMs.
  arXiv  Detail & Related papers  (2023-10-25T14:38:40Z)
• Large Language Model-Aware In-Context Learning for Code Generation [75.68709482932903]
  Large language models (LLMs) have shown impressive in-context learning (ICL) ability in code generation. We propose a novel learning-based selection approach named LAIL (LLM-Aware In-context Learning) for code generation.
  arXiv  Detail & Related papers  (2023-10-15T06:12:58Z)
• Deep Graph Matching and Searching for Semantic Code Retrieval [76.51445515611469]
  We propose an end-to-end deep graph matching and searching model based on graph neural networks. We first represent both natural language query texts and programming language code snippets with the unified graph-structured data. In particular, DGMS not only captures more structural information for individual query texts or code snippets but also learns the fine-grained similarity between them.
  arXiv  Detail & Related papers  (2020-10-24T14:16:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.