Impact of restricted forward greedy feature selection technique on bug prediction
- Published
- Accepted
- Subject Areas
- Data Mining and Machine Learning, Software Engineering
- Keywords
- Restricted Forward Greedy, Feature Selection, Bug Prediction
- Copyright
- © 2015 Kasinathan et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ PrePrints) and either DOI or URL of the article must be cited.
- Cite this article
- 2015. Impact of restricted forward greedy feature selection technique on bug prediction. PeerJ PrePrints 3:e1411v1 https://doi.org/10.7287/peerj.preprints.1411v1
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.
Author Comment
Extension of this work has been published in 8th India Software Engineering Conference.