A Novel Regression Method for Software Defect Prediction with Kernel Methods

In this paper, we propose a novel method based on SVM to predict the number of defects in the files or classes of a software system. To model the relationship between source code similarity and defectiveness, we use SVM with a precomputed kernel matrix. Each value in the kernel matrix shows how much similarity exists between the files or classes of the software system tested. The experiments on 10 Promise datasets indicate that SVM with a precomputed kernel performs as good as the SVM with the usual linear or RBF kernels in terms of the root mean square error (RMSE). The method proposed is also comparable with other regression methods like linear regression and IBK. The results of this study suggest that source code similarity is a good means of predicting the number of defects in software modules. Based on the results of our analysis, the developers can focus on more defective modules rather than on less or non defective ones during testing activities.