Predicting Post-Release Defects in OO Software using Product Metrics

Gabriel de Souza Pereira MoreiraRoberto Pepato MelladoRobson Luis Monteiro JuniorAdilson Marques da CunhaLuiz Alberto Vieira Dias

Software maintenance has consumed more than 50% of development effort and about 90% of software lifecycle. Finding and correcting defects after software delivery have often presented high costs when compared to correct it on previous project phases. Within this context, defect prediction has attracted growing interest from industry and academy. In this study, a survey is conducted, considering two object-oriented systems developed by industry and currently under maintenance. It was proposed and implemented a method for collection and integration of software product metrics for defect prediction. Code and design metrics, at class level, were extracted using static code analysis. The code modules where defects were detected were obtained from corrective maintenance history data. The prediction models presented are based on Multivariate Linear Regression and have used internal quality metrics as predictors and detected defects as predicted variables. This approach can be used to help prioritizing quality activities like testing, inspecting, and refactoring on defect-prone classes.

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