AI-BasedSoftwareDefectPredictors: ApplicationsandBenefits inaCaseStudy Ayse Tosun

Defect predictors are widely used in organizations to predict defects in order to save time and effort as an alternative to other techniques such as manual code reviews. The usage of a defect prediction model in a real-life setting is difficult because it requires software metrics and defect data from past projects to predict the defect-proneness of new projects. It is, on the other hand, very practical because it is easy to apply, can detect defects using less time, and reduces the testing effort. We have built a learning-based defect prediction model for a telecommunications company in the space of one year. In this study, we have briefly explained our model, presented its payoff, and described how we have implemented the model in the company.