mlsanityData to 7 Oct 2026

A RAG Method for Source Code Inquiry Tailored to Long-Context LLMs

arXiv 2404.06082 ↗First posted 9 Apr 2024cs.SEcited by 0

On the map: Retrieval-augmented generation → RAG for code

Abstract (arXiv)

Although the context length limitation of large language models (LLMs) has been mitigated, it still hinders their application to software development tasks. This study proposes a method incorporating execution traces into RAG for inquiries about source code. Small-scale experiments confirm a tendency for the method to contribute to improving LLM response quality.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in RAG for code, one of 15 directions in Retrieval-augmented generation. Builds on 6 of its 7 references that are in the map. First posted on arXiv 9 Apr 2024.

Builds on

6 of 7 references.

References · 6

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2024Gemini 1.5: Unlocking multimodal understanding across millions of tokens of contextcited by 3,175
  3. 2023Using an LLM to Help With Code Understandingcited by 222
  4. 2024Same Task, More Tokens: the Impact of Input Length on the Reasoning Performance of Large Language Modelscited by 150
  5. 2023InferFix: End-to-End Program Repair with LLMscited by 113
  6. 2024Top Leaderboard Ranking = Top Coding Proficiency, Always? EvoEval: Evolving Coding Benchmarks via LLMcited by 38

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  2. 2026Enhancing Large Language Models with Retrieval Augmented Generation for Software Testing and Inspection Automation
  3. 2026Beyond Code Snippets: Benchmarking LLMs on Repository-Level Question Answering
  4. 2025WIP: Leveraging LLMs for Enforcing Design Principles in Student Code: Analysis of Prompting Strategies and RAG ↗
  5. 2025Augmenting Large Language Models with Static Code Analysis for Automated Code Quality Improvements
  6. 2024Are LLMs Correctly Integrated into Software Systems?
  7. 2024Long Context RAG Performance of Large Language Models
  8. 2025When LLMs Meet API Documentation: Can Retrieval Augmentation Aid Code Generation Just as It Helps Developers?