发表机构
School of Software Engineering, Sun Yat-sen University（中山大学软件学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出MAP4CS框架，通过多维数据剪枝仅用5%训练数据实现高效代码检索器微调，性能优于随机采样，可媲美甚至优于全量数据微调，验证了数据中心AI的“少即是多”假设。
AI 中文摘要
检索增强生成（RAG）已成为软件工程领域增强大型语言模型（LLMs）领域特定知识的核心技术。然而，由于大规模代码语料库固有的噪声和冗余，将检索器适配到不断演进的代码仓库仍具挑战性。对完整语料库进行标准微调计算成本高昂，且常因低质量样本的负迁移导致性能欠佳；而简单的随机采样无法保证数据的代表性。为应对这些挑战，本文提出MAP4CS（面向代码搜索的多维感知剪枝），这是一种自适应数据剪枝框架。MAP4CS通过整合句法结构、语义多样性和分布表示，结合严格的基于规则的过滤流程，识别出小型高质量核心子集。在两个大规模数据集上的大量实验表明，MAP4CS仅使用5%的训练数据，始终优于随机采样基线；其性能可与完整数据集微调相媲美，甚至更优，验证了数据中心AI中的“少即是多”假设。此外，语言分析揭示了一种自适应优化机制：MAP4CS自动对冗余语料库执行去重，对混乱语料库执行去噪，构建出词汇多样且信息密集的训练语料库。
英文摘要
Retrieval-Augmented Generation (RAG) has become a cornerstone in software engineering for enhancing Large Language Models (LLMs) with domain-specific knowledge. However, adapting retrievers to evolving code repositories remains challenging due to the noise and redundancy inherent in massive code corpora. Standard fine-tuning on the full corpus is computationally expensive and often leads to sub-optimal performance due to negative transfer from low-quality samples. Conversely, simple random sampling fails to guarantee data representativeness. To address these challenges, we propose MAP4CS (Multi-dimensional Awareness Pruning for Code Search), an adaptive data pruning framework. MAP4CS identifies a small, high-quality core subset by integrating syntactic structure, semantic diversity, and distributional representation, followed by a rigorous rule-based filtering pipeline. Extensive experiments on two large-scale datasets demonstrate that MAP4CS consistently outperforms random sampling baselines using only 5% of the training data. Remarkably, it achieves performance comparable to, or even superior to, fine-tuning on the full dataset, validating the ''less is more'' hypothesis in data-centric AI. Furthermore, linguistic analysis reveals an adaptive optimization mechanism: MAP4CS automatically functions as a de-duplicator for redundant corpora and a denoiser for chaotic ones, constructing a training corpus that is both lexically diverse and information-dense.
