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An important product measure to determine the effectiveness of softwareprocesses is the defect density (DD). In this study, we propose the applicationof support vector regression (SVR) to predict the DD of new software projectsobtained from the International Software Benchmarking Standards Group (ISBSG)Release 2018 data set. Two types of SVR (e-SVR and v-SVR) were applied to trainand test these projects. Each SVR used four types of kernels. The predictionaccuracy of each SVR was compared to that of a statistical regression (i.e., asimple linear regression, SLR). Statistical significance test showed that v-SVRwith polynomial kernel was better than that of SLR when new software projectswere developed on mainframes and coded in programming languages of thirdgeneration

v-SVR Polynomial Kernel for Predicting the Defect Density in New Software Projects