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A Novel Feature Subset Selection Algorithm for Software Defect Prediction

by Reena P, Binu Rajan
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 100 - Number 17
Year of Publication: 2014
Authors: Reena P, Binu Rajan
10.5120/17618-8315

Reena P, Binu Rajan . A Novel Feature Subset Selection Algorithm for Software Defect Prediction. International Journal of Computer Applications. 100, 17 ( August 2014), 39-43. DOI=10.5120/17618-8315

@article{ 10.5120/17618-8315,
author = { Reena P, Binu Rajan },
title = { A Novel Feature Subset Selection Algorithm for Software Defect Prediction },
journal = { International Journal of Computer Applications },
issue_date = { August 2014 },
volume = { 100 },
number = { 17 },
month = { August },
year = { 2014 },
issn = { 0975-8887 },
pages = { 39-43 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume100/number17/17618-8315/ },
doi = { 10.5120/17618-8315 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:30:13.417331+05:30
%A Reena P
%A Binu Rajan
%T A Novel Feature Subset Selection Algorithm for Software Defect Prediction
%J International Journal of Computer Applications
%@ 0975-8887
%V 100
%N 17
%P 39-43
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Feature subset selection is the process of choosing a subset of good features with respect to the target concept. A clustering based feature subset selection algorithm has been applied over software defect prediction data sets. Software defect prediction domain has been chosen due to the growing importance of maintaining high reliability and high quality for any software being developed. A software quality prediction model is built using software metrics and defect data collected from a previously developed system release or similar software projects. Upon validation of such a model, it could be used for predicting the fault-proneness of program modules that are currently under development. The proposed clustering based algorithm for feature selection uses minimum spanning tree based method to cluster features. And then the algorithm is applied over four different data sets and its impact is analyzed.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Relevant features Redundant Features Minimum spanning tree Tree partition graph based clustering Software defect prediction Naïve Bayes classifier Decision tree classifier

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