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.

• Type: Conference
• Year: 2006
• Where: AICCSA
• Authors: Stamatia Bibi, Grigorios Tsoumakas, Ioannis Stamelos, Ioannis P. Vlahavas
• Keywords: AICCSA 2006, Defects Called Regression, Hardware, Machine Learning, Machine Learning Approach