||Author(s): S. Bibi, G. Tsoumakas, I. Stamelos, I. Vlahavas.
Title: “Software Defect Prediction Using Regression via Classification”.
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Proc. 4th ACS/IEEE International Conference on Computer Systems and Applications, AICCSA '06, (accepted for presentation), pp. 330- 336, 2006.
Abstract: 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.