v-SVR Polynomial Kernel for Predicting the Defect Density in New Software Projects
Abstract
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
Keywords
Cite
@article{arxiv.1901.03362,
title = {v-SVR Polynomial Kernel for Predicting the Defect Density in New Software Projects},
author = {Cuauhtemoc Lopez-Martin and Mohammad Azzeh and Ali Bou Nassif and Shadi Banitaan},
journal= {arXiv preprint arXiv:1901.03362},
year = {2019}
}
Comments
6 pages, accepted at Special Session: ML for Predictive Models in Eng. Applications at the 17th IEEE International Conference on Machine Learning and Applications, 17th IEEE ICMLA 2018