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Retrieval Augmented Code Generation and Summarization

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arxiv 2108.11601 v2 pith:5XXJ7JQD submitted 2021-08-26 cs.SE cs.CL

Md Rizwan Parvez , Wasi Uddin Ahmad , Saikat Chakraborty , Baishakhi Ray , Kai-Wei Chang This is my paper
classification cs.SEcs.CL
keywords coderetrievalgenerationaugmenteddeveloperssoftwaresummariessummarization
verification ladder T0 review T1 audit T2 compute T3 formal
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Software developers write a lot of source code and documentation during software development. Intrinsically, developers often recall parts of source code or code summaries that they had written in the past while implementing software or documenting them. To mimic developers' code or summary generation behavior, we propose a retrieval augmented framework, REDCODER, that retrieves relevant code or summaries from a retrieval database and provides them as a supplement to code generation or summarization models. REDCODER has a couple of uniqueness. First, it extends the state-of-the-art dense retrieval technique to search for relevant code or summaries. Second, it can work with retrieval databases that include unimodal (only code or natural language description) or bimodal instances (code-description pairs). We conduct experiments and extensive analysis on two benchmark datasets of code generation and summarization in Java and Python, and the promising results endorse the effectiveness of our proposed retrieval augmented framework.

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Cited by 9 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. RepoReasoner: Evaluating Repository-Level Code Reasoning Ability of Long-Context Language Models

    cs.SE 2026-07 conditional novelty 7.0 of 10

    RepoReasoner is a repository-level code-reasoning benchmark with output-prediction and call-chain tasks; the best LLM reaches only 69.1% Pass@1 even with oracle context, with low recall in dependency tracing.

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  3. Beyond "What to Retrieve": Uncertainty in Retrieval-Augmented Code Generation

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    Uncertainty-aware multi-source retrieval improves GPT repository-level code selection over plain RAG but matches verification-and-repair alone and is backend- and context-dependent.

  4. PennySynth: RAG-Driven Data Synthesis for Automated Quantum Code Generation

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    Retrieval over a 13,389-example verified PennyLane corpus raises QHack pass@5 from 36/43/24% to 64/68/52% across 2022–2024 with Claude Sonnet 4.6.

  5. GRACE: Graph-Guided Repository-Aware Code Completion through Hierarchical Code Fusion

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    GRACE combines a multi-level code graph, hybrid text-structure retrieval, and graph fusion to improve repository-level code completion over vanilla and graph-based RAG baselines.

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