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<p>In software engineering, effective documentation is crucial for understanding complex codebases, yet it often remains incomplete or outdated, hindering developer productivity. This paper introduces LEDGE (Leveraging Dependency Graphs for Enhanced Context Aware Documentation Generation), a novel framework that integrates dependency graphs with large language models (LLMs) to automate the generation of structured, context aware software documentation. By leveraging GraphRAG, LEDGE captures semantic and structural relationships within codebases, enabling precise documentation that highlights architectural insights and module dependencies. Our methodology employs a parser based approach to construct dependency graphs, stored in MemGraph using Cypher queries, and utilizes vector embeddings for similarity based retrieval. Evaluated on diverse open source repositories, LEDGE demonstrates comparable semantic alignment with existing documentation while providing enhanced structural context, as evidenced by qualitative and quantitative analyses. The framework enhances software maintainability, developer onboarding, and knowledge transfer, offering a scalable solution for modern software development. Our code and data are available at <a href="https://github.com/MihirRajeshPanchal/LEDGE">https://github.com/MihirRajeshPanchal/LEDGE</a>.</p>

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LEDGE : Leveraging dependency graphs for enhanced context aware documentation generation

  • 期刊论文

  • 发表日期：

    2026年2月3日

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  • Mihir Panchal,
  • Arnav Deo,
  • Varad Prabhu,
  • Prinkal Doshi,
  • Chetashri Bhadane,
  • Pranit Bari

摘要

In software engineering, effective documentation is crucial for understanding complex codebases, yet it often remains incomplete or outdated, hindering developer productivity. This paper introduces LEDGE (Leveraging Dependency Graphs for Enhanced Context Aware Documentation Generation), a novel framework that integrates dependency graphs with large language models (LLMs) to automate the generation of structured, context aware software documentation. By leveraging GraphRAG, LEDGE captures semantic and structural relationships within codebases, enabling precise documentation that highlights architectural insights and module dependencies. Our methodology employs a parser based approach to construct dependency graphs, stored in MemGraph using Cypher queries, and utilizes vector embeddings for similarity based retrieval. Evaluated on diverse open source repositories, LEDGE demonstrates comparable semantic alignment with existing documentation while providing enhanced structural context, as evidenced by qualitative and quantitative analyses. The framework enhances software maintainability, developer onboarding, and knowledge transfer, offering a scalable solution for modern software development. Our code and data are available at https://github.com/MihirRajeshPanchal/LEDGE.