mlsanityData to 13 Oct 2026

Hierarchical Repository-Level Code Summarization for Business Applications Using Local LLMs

arXiv 2501.07857 ↗First posted 14 Jan 2025cs.SE cs.AIcited by 6

Not on the map. Why?

Abstract (arXiv)

In large-scale software development, understanding the functionality and intent behind complex codebases is critical for effective development and maintenance. While code summarization has been widely studied, existing methods primarily focus on smaller code units, such as functions, and struggle with larger code artifacts like files and packages. Additionally, current summarization models tend to emphasize low-level implementation details, often overlooking the domain and business context that are crucial for real-world applications. This paper proposes a two-step hierarchical approach for repository-level code summarization, tailored to business applications. First, smaller code units such as functions and variables are identified using syntax analysis and summarized with local LLMs. These summaries are then aggregated to generate higher-level file and package summaries. To ensure the summaries are grounded in business context, we design custom prompts that capture the intended purpose of code artifacts based on the domain and problem context of the business application. We evaluate our approach on a business support system (BSS) for the telecommunications domain, showing that syntax analysis-based hierarchical summarization improves coverage, while business-context grounding enhances the relevance of the generated summaries.

Key facts. Cited by 6 papers in the map (data through 13 Oct 2026). Builds on 18 of its 36 references that are in the map. First posted on arXiv 14 Jan 2025.

Builds on

18 of 36 references.

References · 18

  1. 2023GPT-4 Technical Reportcited by 18,278
  2. 2024The Llama 3 Herd of Modelscited by 14,486
  3. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,480
  4. 2023G-Eval: NLG Evaluation using GPT-4 with Better Human Alignmentcited by 2,228
  5. 2023Code Llama: Open Foundation Models for Codecited by 2,130
  6. 2019Language Models as Knowledge Bases?cited by 1,931
  7. 2023ChatDev: Communicative Agents for Software Developmentcited by 901
  8. 2021CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generationcited by 743
  9. 2019CodeSearchNet Challenge: Evaluating the State of Semantic Code Searchcited by 651
  10. 2024StarCoder 2 and The Stack v2: The Next Generationcited by 499
  11. 2024DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligencecited by 328
  12. 2020A Transformer-based Approach for Source Code Summarizationcited by 132
6 more
  1. 2024AgileCoder: Dynamic Collaborative Agents for Software Development based on Agile Methodologycited by 45
  2. 2024A Deep Dive into Large Language Models for Automated Bug Localization and Repaircited by 39
  3. 2024Hybrid Automated Program Repair by Combining Large Language Models and Program Analysiscited by 22
  4. 2021CommitBERT: Commit Message Generation Using Pre-Trained Programming Language Modelcited by 16
  5. 2023Developer-Intent Driven Code Comment Generationcited by 16
  6. 2024Automated Commit Message Generation with Large Language Models: An Empirical Study and Beyondcited by 10

Built on by

Cited by year (2026 to 13 Oct)

Citing papers per year: 2025: 1; 2026: 5.

6 papers.