Multivariate logistic regression prediction of fault-proneness in software modules

This paper explores additional features, provided by stepwise logistic regression, which could further improve performance of fault predicting model. Three different models have been used to predict fault-proneness in NASA PROMISE data set and have been compared in terms of accuracy, sensitivity and false alarm rate: one with forward stepwise logistic regression, one with backward stepwise logistic regression and one without stepwise selection in logistic regression. Despite an obvious trade-off between sensitivity and false alarm rate, we can conclude that backward stepwise regression gave the best model.