A Novel Regression Method for Software Defect Prediction with Kernel Methods

Ahmet Okutan, Olcay Taner Yıldız

2013

Abstract

In this paper, we propose a novel method based on SVM to predict the number of defects in the files or classes of a software system. To model the relationship between source code similarity and defectiveness, we use SVM with a precomputed kernel matrix. Each value in the kernel matrix shows how much similarity exists between the files or classes of the software system tested. The experiments on 10 Promise datasets indicate that SVM with a precomputed kernel performs as good as the SVM with the usual linear or RBF kernels in terms of the root mean square error (RMSE). The method proposed is also comparable with other regression methods like linear regression and IBK. The results of this study suggest that source code similarity is a good means of predicting the number of defects in software modules. Based on the results of our analysis, the developers can focus on more defective modules rather than on less or non defective ones during testing activities.

Paper Citation

in Harvard Style

Okutan A. and Taner Yıldız O. (2013). A Novel Regression Method for Software Defect Prediction with Kernel Methods . In Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-8565-41-9, pages 216-221. DOI: 10.5220/0004290002160221

in Bibtex Style

@conference{icpram13,
author={Ahmet Okutan and Olcay Taner Yıldız},
title={A Novel Regression Method for Software Defect Prediction with Kernel Methods},
booktitle={Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2013},
pages={216-221},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004290002160221},
isbn={978-989-8565-41-9},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - A Novel Regression Method for Software Defect Prediction with Kernel Methods
SN - 978-989-8565-41-9
AU - Okutan A.
AU - Taner Yıldız O.
PY - 2013
SP - 216
EP - 221
DO - 10.5220/0004290002160221