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Multivariate logistic regression prediction of fault-proneness in software modules

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3 Author(s)
Mausa, G. ; Fac. of Eng., Univ. of Rijeka, Rijeka, Croatia ; Grbac, T.G. ; Basic, B.D.

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.

Published in:

MIPRO, 2012 Proceedings of the 35th International Convention

Date of Conference:

21-25 May 2012