When Retriever Meets Generator: A Joint Model for Code Comment Generation

• URL: http://arxiv.org/abs/2507.12558v2
• Date: Thu, 24 Jul 2025 07:07:10 GMT
• Title: When Retriever Meets Generator: A Joint Model for Code Comment Generation
• Authors: Tien P. T. Le, Anh M. T. Bui, Huy N. D. Pham, Alessio Bucaioni, Phuong T. Nguyen,
• Abstract summary: RAGSum is built on top offuse retrieval and generation using a single CodeT5 backbone.<n>A contrastive pre-training phase shapes code embeddings for nearest-neighbor search.<n>A lightweight self-refinement loop is deployed to polish the final output.
• Score: 3.6781644685120924
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Automatically generating concise, informative comments for source code can lighten documentation effort and accelerate program comprehension. Retrieval-augmented approaches first fetch code snippets with existing comments and then synthesize a new comment, yet retrieval and generation are typically optimized in isolation, allowing irrelevant neighbors topropagate noise downstream. To tackle the issue, we propose a novel approach named RAGSum with the aim of both effectiveness and efficiency in recommendations. RAGSum is built on top offuse retrieval and generation using a single CodeT5 backbone. We report preliminary results on a unified retrieval-generation framework built on CodeT5. A contrastive pre-training phase shapes code embeddings for nearest-neighbor search; these weights then seed end-to-end training with a composite loss that (i) rewards accurate top-k retrieval; and (ii) minimizes comment-generation error. More importantly, a lightweight self-refinement loop is deployed to polish the final output. We evaluated theframework on three cross-language benchmarks (Java, Python, C), and compared it with three well-established baselines. The results show that our approach substantially outperforms thebaselines with respect to BLEU, METEOR, and ROUTE-L. These findings indicate that tightly coupling retrieval and generationcan raise the ceiling for comment automation and motivateforthcoming replications and qualitative developer studies.

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