发表机构
École de Technologie Supérieure, University of Quebec, Montreal, Canada（魁北克大学高级技术学院，蒙特利尔，加拿大）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对跨项目缺陷预测中训练与目标项目分布差异问题，提出两阶段多分类器系统选择方案进行模块级模型选择，实验证明该方法在多数场景下优于现有方法。
AI 中文摘要
跨项目缺陷预测（CPDP）利用来自外部训练项目的数据构建模型来预测目标项目中的模块。传统CPDP方法受训练与目标项目分布差异影响。本文提出新颖框架，采用两阶段多分类器系统（MCS）选择方案，项目级和模块级各一阶段。第一阶段评估多种MCS配置，获取多样分类器。第二阶段在测试时为目标项目各模块选择最合适分类器。实验表明该方法在多数场景优于现有方法。
英文摘要
Cross-Project Defect Prediction (CPDP) involves building models using data from external projects, called training projects, to predict modules from the target project. However, traditional CPDP methods suffer from the distribution shift between training and target projects that affects the model's performance. This paper proposes a novel CPDP framework that addresses this issue by proposing a two-stage multiple classifier system (MCS) selection scheme: one working at the project level and another at the module level. In the first stage, the framework evaluates multiple possible MCS configurations to find one that covers and generalizes well across multiple training projects. Consequently, the proposal is likely to obtain a diverse set of classifiers, each specialized in tackling software modules with distinct characteristics. The second selection stage operates at test time, selecting the most competent classifiers to predict each new module in the target project. Unlike previous approaches that apply the same classifiers to the entire target project, the proposed framework performs module-level model selection. This way, the system is more robust to changes in distributions between training and target projects because the selected set of classifiers is module-dependent. Our experimental results using 82 projects from four different CPDP benchmark datasets demonstrate that the proposed approach outperforms the state-of-the-art CPDP methods in most scenarios. The code, dataset, and further details about the proposed method are publicly available at https://github.com/jsaj/Multi_DES.
CommentsPaper accepted to the 2026 International Joint Conference on Neural Networks
