  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
