Impact of restricted forward greedy feature selection technique on bug prediction
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Abstract
Several change metrics and source code metrics have been introduced and proved to be effective in bug prediction. Researchers performed comparative studies of bug prediction models built using the individual metrics as well as combination of these metrics. In this paper, we investigate the impact of feature selection in bug prediction models by analyzing the misclassification rates of these models with and without feature selection in place. We conduct our experiments on five open source projects by considering numerous change metrics and source code metrics. And this study aims to figure out the reliable subset of metrics that are common amongst all projects.
Cite this as
2015. Impact of restricted forward greedy feature selection technique on bug prediction. PeerJ PrePrints 3:e1411v1 https://doi.org/10.7287/peerj.preprints.1411v1Author comment
Extension of this work has been published in 8th India Software Engineering Conference.
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Competing Interests
The authors declare that they have no competing interests.
Author Contributions
Muthukumaran Kasinathan conceived and designed the experiments, performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, wrote the paper, prepared figures and/or tables, performed the computation work.
Lalita Bhanu Murthy Neti conceived and designed the experiments, performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, wrote the paper, prepared figures and/or tables, performed the computation work, reviewed drafts of the paper.
Funding
The authors received no funding for this work.
