Software science metrics (SSM) have been widely used as predictors of software defects. The usage of SSM is an effect of correlation of size and complexity metrics with number of defects. The SSM have been proposed keeping in view the procedural paradigm and structural nature of the programs. There has been a shift in software development paradigm from procedural to object oriented (OO) and SSM have been used as defect predictors of OO software as well. However, the effectiveness of SSM in OO software needs to be established. This paper investigates the effectiveness of use of SSM for: a) classification of defect prone modules in OO software b) prediction of number of defects. Various binary and numeric classification models have been applied on dataset kc1 with class level data to study the role of SSM. The results show that the removal of SSM from the set of independent variables does not significantly affect the classification of modules as defect prone and the prediction of number of defects. In most of the cases the accuracy and mean absolute error has improved when SSM were removed from the set of independent variables. The results thus highlight the ineffectiveness of use of SSM in defect prediction in OO software.
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