The ability to automatically classify source code repositories with ''topics'' that reflect their content and purpose is very useful, especially when navigating or searching through large software collections. However, existing approaches often rely heavily on README files and other metadata, which are frequently missing, limiting their applicability in real-world large-scale settings. We present DRAGON, a repository classifier designed for very large and diverse software collections. It operates entirely on lightweight signals commonly stored in version control systems: file and directory names, and optionally the README when available. In repository classification at scale, DRAGON improves F1@5 from 54.8% to 60.8%, surpassing the state of the art. DRAGON remains effective even when README files are absent, with performance degrading by only 6% w.r.t. when they are present. This robustness makes it practical for real-world settings where documentation is sparse or inconsistent. Furthermore, many of the remaining classification errors are near misses, where predicted labels are semantically close to the correct topics. This property increases the practical value of the predictions in real-world software collections, where suggesting a few related topics can still guide search and discovery. As a byproduct of developing DRAGON, we also release the largest open dataset to date for repository classification, consisting of 825 thousand repositories with associated ground-truth topics, sourced from the Software Heritage archive, providing a foundation for future large-scale and language-agnostic research on software repository understanding.
DRAGON: Domain-specific Robust Automatic Data Generation for RAG Optimization
Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）；School of Computer Science, Peking University（北京大学计算机学院）；The Chinese University of Hong Kong（香港中文大学）；School of Computer Science, Sun Yat-sen University（中山大学计算机学院）；School of Software & Microelectronics, Peking University（北京大学软件与微电子学院）
Do Coding Agents Reuse Existing Code or Reinvent the Wheel?
Peking University（北京大学）；Fudan University（复旦大学）；Zhongguancun Academy（中关村学院）；Shanghai Jiao Tong University（上海交通大学）；Tsinghua University（清华大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））
The Rio Hortega University Hospital Glioblastoma dataset: a comprehensive collection of preoperative, early postoperative and recurrence MRI scans (RHUH-GBM)
本文介绍了来自MSR 2021 Hackcraft的结果。我们的团队调查包含已知安全漏洞的文件/项目，以及它们在开源软件中的存储库中的分布情况。这些安全漏洞可能通过代码重用传播，即使在不同版本的代码中修复了漏洞。我们利用World of Code基础设施从几乎完整的开源软件集合中发现文件级代码重复。本文描述了一种方法和一组工具，用于查找使用已知易受攻击文件的所有开源项目以及这些文件的任何以前版本。
