一句话：提出统一集成 20 种数据选择方法的可扩展框架 DSBox，支持高效深度代码学习的数据筛选与灵活扩展。
深度代码模型的训练遵循以数据为中心的范式，需要高质量数据集，但软件任务的标注成本高昂，成为构建数据集的主要瓶颈。已有多种数据选择方法用于筛选并标注对训练重要的样本，然而缺乏统一工具支持这些方法的灵活使用，阻碍了实践与后续研究。为此，作者提出 DSBox——一个轻量且可扩展的框架，统一集成了 20 种已发表的数据选择方法，按不确定性（uncertainty）、代表性（representativeness）与基于质量（quality-based）三类组织，并提供统一接口以便替换与组合。评估表明，在代码漏洞检测任务上，主动学习（active learning）方法优于近期为大型语言模型设计的选择技术。工具与演示视频已公开。
Deep Learning has achieved remarkable advancements in various software engineering tasks and gained huge attention in the community. Following a data-centric paradigm, the preparation of code models requires high-quality datasets for the model training. However, constructing such datasets, especially for software tasks, is costly mainly due to the data labeling process. To address this challenge, multiple data selection methods have been proposed to identify and label data samples that are important for training. Despite this potential, unfortunately, there are limited tools to support the flexible usage of data selection methods, hindering their practical usage and future research in this domain. To bridge this gap, we introduce DSBox, a lightweight yet extensible framework that unifies 20 published selection methods, covering three categories: uncertainty, representativeness, and quality-based methods. Evaluation demonstrates that active learning methods outperform recently proposed techniques (designed for large language models) on the code vulnerability detection task. The tool, as well as a demonstration video, are available on the project website https://sites.google.com/view/dsbox2025.
DOI 原文 ·
@inproceedings{paperbot9408,
title = {DSBox: A Data Selection Framework for Efficient Deep Code Learning},
author = {Xinyang Liu and Lili Quan and Qiang Hu},
booktitle = {ASE},
year = {2025},
doi = {10.1109/ase63991.2025.00375}
}