关键词: 跨项目缺陷预测, 迁移学习, 基于实例的迁移, 自适应增强

Abstract: Cross-project defect prediction is considered as an effective means for solving the data shortage early in the project. Unfortunately, the performance of cross-project defect prediction is generally poor largely because of project variation. Focusing on this issue, this paper proposes a cross-project defect prediction approach based on instance transfer. The approach uses transfer learning and boosting technology to extract and transfer the training dataset high-related with target dataset from other projects, and builds a stronger combined classification model. The experimental results on PROMISE datasets show that, the proposed approach is superior to single-source single-target boosting methods with higher precision and recall; and in early phase with short data, it can achieve similar or better prediction results than intra-project approach with rich data.

Key words: cross-project defect prediction, transfer learning, instance-based transfer, boosting