v-SVR Polynomial Kernel for Predicting the Defect Density in New Software Projects
This work addresses software quality prediction for developers and managers, but it is incremental as it applies existing methods to a specific dataset.
The study tackled predicting defect density in new software projects using support vector regression (SVR) on ISBSG data, finding that v-SVR with a polynomial kernel outperformed simple linear regression for projects on mainframes with third-generation languages.
An important product measure to determine the effectiveness of software processes is the defect density (DD). In this study, we propose the application of support vector regression (SVR) to predict the DD of new software projects obtained from the International Software Benchmarking Standards Group (ISBSG) Release 2018 data set. Two types of SVR (e-SVR and v-SVR) were applied to train and test these projects. Each SVR used four types of kernels. The prediction accuracy of each SVR was compared to that of a statistical regression (i.e., a simple linear regression, SLR). Statistical significance test showed that v-SVR with polynomial kernel was better than that of SLR when new software projects were developed on mainframes and coded in programming languages of third generation