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Discrimination Analysis for Predicting Defect-Prone Software Modules
2014
Journal of Applied Mathematics
Software defect prediction studies usually build models without analyzing the data used in the procedure. As a result, the same approach has different performances on different data sets. In this paper, we introduce discrimination analysis for providing a good method to give insight into the inherent property of the software data. Based on the analysis, we find that the data sets used in this field have nonlinearly separable and class-imbalanced problems. Unlike the prior works, we try to
doi:10.1155/2014/675368
fatcat:2acaohsuergerhedjgvklehrza