基于多核字典学习的软件缺陷预测

提出一种多核字典学习方法,用以对软件模块是否存在缺陷进行预测。用于软件缺陷预测的历史数据具有结构复杂、类不平衡的特点,用多个核函数构成的合成核将这些数据映射到一个高维特征空间,通过对多核字典基的选择,得到一个类别平衡的多核字典,用以对新的软件模块进行分类和预测,并判定其中是否存在缺陷。在NASA MDP数据集上的实验表明,与其他软件缺陷预测方法相比,多核字典学习方法能够针对软件缺陷历史数据结构复杂、类不平衡的特点,较好地解决软件缺陷预测问题。

Multiple Kernel Dictionary Learning for Software Defect Prediction

A multiple kernel dictionary learning approach for software defect prediction was proposed.Software historical defect data have complicated structure and marked characteristic of class-imbalance.Multiple kernel learning is an effective technique in the field of machine learning which can map the historical defect data to a higher-dimensional feature space and make them express better.We got a multiple kernel dictionary learning classifier which has the advantages of both multiple kernel learning and dictionary learning.The widely used datasets from NASA MDP datasets are employed as test data to evaluate the performance of all compared methods.Experimental results demonstrate the effectiveness of the proposed multiple kernel dictionary learning approach for the software defect prediction task.