CAJ | 학술논문

软件缺陷预测是改善软件开发质量、提高测试效率的重要途径.文中分析了软件缺陷预测的特点,同时针对当前软件缺陷预测中存在特征冗余问题和类不平衡问题进行了深入研究.首先为了解决软件模块中的特征冗余问题给软件缺陷预测造成困难,提高对软件缺陷预测的准确率,采用基于代价敏感的拉普拉斯特征映射方法(CSLE)对原样本空间进行降维,改进拉普拉斯算法(LE)中的距离度量方式,提高降维映射精度;然后通过基于代价敏感的神经网络的方法(CSB-PNN)对软件模块进行分类,调整BP神经网络的权值和偏置参数,使BP神经网络对有缺陷软件模块的误分更加敏感,进一步提高分类效果.在NASA软件缺陷标准数据集上与最新的几种软件缺陷预测方法相比,文中提出的方法能够有效提高有缺陷样本的召回率和F-measure值.
연건결함예측시개선연건개발질량、제고측시효솔적중요도경.문중분석료연건결함예측적특점,동시침대당전연건결함예측중존재특정용여문제화류불평형문제진행료심입연구.수선위료해결연건모괴중적특정용여문제급연건결함예측조성곤난,제고대연건결함예측적준학솔,채용기우대개민감적랍보랍사특정영사방법(CSLE)대원양본공간진행강유,개진랍보랍사산법(LE)중적거리도량방식,제고강유영사정도;연후통과기우대개민감적신경망락적방법(CSB-PNN)대연건모괴진행분류,조정BP신경망락적권치화편치삼수,사BP신경망락대유결함연건모괴적오분경가민감,진일보제고분류효과.재NASA연건결함표준수거집상여최신적궤충연건결함예측방법상비,문중제출적방법능구유효제고유결함양본적소회솔화F-measure치.
Software defect prediction is an important way to improve the quality of software development and raise the testing efficiency. In this paper,analyze the characteristics of software defect prediction and focus on the research of redundancy features and the imbalance class problem existed in current software defect. In order to solve the difficulty of software defect prediction caused by redundancy fea-tures in software modules,improving the accuracy for software defect prediction,adopt a new method named Cost-Censitive Laplacian Eigenmaps (CSLE) to reduce the dimensionality of original sample space, improving the distance measurement method of Laplacian Eigenmaps (LE) to enhance the dimension reduction mapping accuracy. In addition,propose a new method named Cost Sensitive Back Propagation Neural Network (CSBPNN) to classify the software module,adjusting the weights and bias parameters of BP neural net-work,which makes the error of BP neural network to flawed software modules points more sensitive,further improving the classification effect. Compared with the latest several software defect prediction methods on NASA software datasets,prove that this method can im-prove the recall rate and F-measure value in software defect prediction.