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Regression via Classification applied on software defect estimation
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DOI：10.1016/j.eswa.2007.02.012.png)
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In this paper we apply Regression via Classification (RvC) to the problem of estimating the number of software defects. This approach apart from a certain number of faults, it also outputs an associated interval of values, within which this estimate lies with a certain confidence. RvC also allows the production of comprehensible models of software defects exploiting symbolic learning algorithms. To evaluate this approach we perform an extensive comparative experimental study of the effectiveness of several machine learning algorithms in two software data sets. RvC manages to get better regression error than the standard regression approaches on both datasets. (c) 2007 Elsevier Ltd. All rights reserved.
Keyword:
software quality
software metrics
software fault estimation
Regression via Classification
ISBSG data set
machine learning
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期刊
IF:
7.5
论文数:
3.0W
被引数:
10.2W
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