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2006
IEEE

Software Defect Prediction Using Regression via Classification

11 years 1 months ago
Software Defect Prediction Using Regression via Classification
In this paper we apply a machine learning approach to the problem of estimating the number of defects called Regression via Classification (RvC). RvC initially automatically discretizes the number of defects into a number of fault classes, then learns a model that predicts the fault class of a software system. Finally, RvC transforms the class output of the model back into a numeric prediction. This approach includes uncertainty in the models because apart from a certain number of faults, it also outputs an associated interval of values, within which this estimate lies, with a certain confidence. To evaluate this approach we perform a comparative experimental study of the effectiveness of several machine learning algorithms in a software dataset. The data was collected by Pekka Forselious and involves applications maintained by a bank of Finland.
Stamatia Bibi, Grigorios Tsoumakas, Ioannis Stamel
Added 13 Oct 2010
Updated 13 Oct 2010
Type Conference
Year 2006
Where AICCSA
Authors Stamatia Bibi, Grigorios Tsoumakas, Ioannis Stamelos, Ioannis P. Vlahavas
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