基于迁移学习的软件缺陷预测

传统软件缺陷预测方法在解决跨项目缺陷预测过程中适应能力不足,主要是因为源项目和目标项目之间存在不同的特征分布.为了解决这个问题,提出一种新的加权贝叶斯迁移学习算法,算法首先收集训练数据和测试数据的特征信息,然后计算特征差异,将不同项目数据之间差异转化为训练数据权重,最后基于这些权重数据建立预测模型.在8个开源项目数据集上进行实验比较,实验结果表明与其他方法相比本文方法显著提高跨项目缺陷预测性能.

Transfer Learning for Software Defect Prediction

The traditional software defect prediction methods have weak adaptive ability for cross-project defect prediction, largely because of feature distribution differences between the source and target projects.In order to resolve this problem, we propose a novel weighted naive Bayes transfer learning algorithm.Firstly, the feature information of the test data and training data are collected;next, our solution computes feature differences, and transfers cross-project data differences into the weights of the training data;finally, on these weighted data, the defect prediction model is built.Our experiments are conducted on eight open-source projects, and experimental results demonstrate that our method significantly improves cross-project defect prediction performance, compared to other methods.