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Software Defect Prediction using Boosting Techniques

by V. Jayaraj, N. Saravana Raman
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 65 - Number 13
Year of Publication: 2013
Authors: V. Jayaraj, N. Saravana Raman
10.5120/10981-6130

V. Jayaraj, N. Saravana Raman . Software Defect Prediction using Boosting Techniques. International Journal of Computer Applications. 65, 13 ( March 2013), 1-4. DOI=10.5120/10981-6130

@article{ 10.5120/10981-6130,
author = { V. Jayaraj, N. Saravana Raman },
title = { Software Defect Prediction using Boosting Techniques },
journal = { International Journal of Computer Applications },
issue_date = { March 2013 },
volume = { 65 },
number = { 13 },
month = { March },
year = { 2013 },
issn = { 0975-8887 },
pages = { 1-4 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume65/number13/10981-6130/ },
doi = { 10.5120/10981-6130 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:18:39.225953+05:30
%A V. Jayaraj
%A N. Saravana Raman
%T Software Defect Prediction using Boosting Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 65
%N 13
%P 1-4
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Identification and removal of software defects is tedious and time consuming for software development. Improperly planned projects could have defects and the time spent to spot and fix them requires more than the code development time. A reverse engineering sub-area is identification of modules necessitating re-engineering, focusing on faulty modules prediction based on existing information sources like documentation and source code. Predicting defective module is essential in maintenance and reuse by simplifying system working with information and reusable parts localization. In software defect prediction, predictive models estimation is based on code attributes to assess software modules containing errors likelihood. In this paper, the classification accuracy of Boosting techniques for software defect prediction based on the KC1 dataset is investigated.

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Index Terms

Computer Science
Information Sciences

Keywords

Software Defect Prediction KC1 Dataset Bagging

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