可缓解类重叠问题的跨版本软件缺陷预测方法

针对软件缺陷预测过程中未充分使用源代码语义特征以及训练数据集中的类重叠问题, 提出一种面向类重叠的跨版本软件缺陷深度特征学习方法. 该方法采用混合式最近邻清理策略缓解深度学习语义特征中存在的类重叠问题. 在PROMISE公开数据集上进行测试的结果表明, 该策略能提升基于深度语义学习的软件缺陷预测性能, 分类性能最多在中值上提升14.8%. 实验结果表明, 在跨版本深度缺陷预测问题中可采用混合式最近邻清理策略缓解类重叠问题.

Cross-Version Software Defect Prediction Method for Relieving Class Overlap Problem

Aiming at the problem that semantic features of source code were not fully used in the process of software defect prediction and class overlap in training data set, we proposed a cross-version software defect deep feature learning method for class overlap. This method used a hybrid nearest neighbor cleaning strategy to alleviate class overlap problem in deep learning semantic features. The test results on open data set PROMISE show that this strategy can improve the performance of software defect prediction based on deep semantic learning, and the classification performance can be improved by 14.8% at most in the median value. The experimental results show that a hybrid nearest neighbor cleaning strategy can be used to alleviate the class overlap problem in the cross-version deep defect prediction problem.