Predicting Number of Software Defects and Defect Remediation Time to Minimize Leakage and Allocate Rework Efforts in an Upcoming Software Release
Open AccessInformation Technology companies spend substantial resources fixing the damage caused by software defects. Defect remediation time is an important metric in allocating rework efforts (resources) for fixing the defects. The aim of this praxis is to use statistical learning models to predict the number of defects and defect remediation time prior to testing. Obtaining information from these models is valuable because it gives software engineering managers a better method by which to minimize the leakage of the defects and allocate rework efforts in an upcoming software release.The predictors for number of defects are: total number of components delivered, code size (lines of code), total number of developers working on code components, total number of requirements, and total number of test cases. The predictors for defect remediation time are: total number of test cases, total number of requirements, number of defects, and code size. Previous studies have used these predictors individually in predicting the number of defects and defect remediation time. However, none of the previous studies have considered combining all the predictors in their predictions. This praxis addresses a gap in previous software industry research by proposing the number of defects and defect remediation time predictions using 202 mainframe languages software projects over 4 years of a dataset containing 1,143 defects, and the combined influence of all the predictors. The proposed statistical learning models used in this praxis are negative binomial regression, multiple linear regression, random forest, and support vector machine. If the number of defects and defect remediation time can be predicted, both software managers and researchers will benefit from this research by applying statistical learning models to minimize defect leakage and allocate rework efforts.
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