We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 November 2024
Call for Paper
December Edition
IJCA solicits high quality original research papers for the upcoming December edition of the journal. The last date of research paper submission is 20 November 2024

Submit your paper
Know more
The week's pick
Responsive image
An Efficient Restoration Method for the Faint Dot Matrix Images

Ao Zhu Peng Cao
Random Articles
Reseach Article

Empirical Studies to Predict Fault Proneness: A Review

by Puja Saxena, Monika Saini
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 22 - Number 8
Year of Publication: 2011
Authors: Puja Saxena, Monika Saini
10.5120/2600-3626

Puja Saxena, Monika Saini . Empirical Studies to Predict Fault Proneness: A Review. International Journal of Computer Applications. 22, 8 ( May 2011), 41-45. DOI=10.5120/2600-3626

@article{ 10.5120/2600-3626,
author = { Puja Saxena, Monika Saini },
title = { Empirical Studies to Predict Fault Proneness: A Review },
journal = { International Journal of Computer Applications },
issue_date = { May 2011 },
volume = { 22 },
number = { 8 },
month = { May },
year = { 2011 },
issn = { 0975-8887 },
pages = { 41-45 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume22/number8/2600-3626/ },
doi = { 10.5120/2600-3626 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:08:54.161381+05:30
%A Puja Saxena
%A Monika Saini
%T Empirical Studies to Predict Fault Proneness: A Review
%J International Journal of Computer Applications
%@ 0975-8887
%V 22
%N 8
%P 41-45
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Empirical validations of software metrics are used to predict software quality in the past years. This paper provides a review of empirical studies to predict software fault proneness with a specific focus on techniques used. The paper highlights the milestone studies done from 1995 to 2010 in this area. Results show that use of machine learning languages have started.This paper reviews works done in the field of software fault prediction studies. The study is concentrated on statistical techniques and their usage to predict fault proneness. The conclusion drawn is the future studies should use more of class level metrics and the best technique to derive fault predictors amongst statistical techniques is logistic regression.

References
  1. Basili, V., Briand, L., & Melo, W. A validation of object oriented design metrics as quality indicators. IEEE Transactions on Software Engineering, 1996, 22(10), pp.751–761.
  2. Briand, L., Daly, J., Porter, V., & Wust, J.A comprehensive empirical validation of design measures for Object Oriented Systems. Proceeding METRICS '98 Proceedings of the 5th International Symposium on Software Metrics IEEE Computer Society Washington, DC, USA. 1998.
  3. Tang, M. H., Kao, M. H., & Chen, M. H. An empirical study on object-oriented metrics. In Proceedings of 6th IEEE International Symposium on Software Metrics. 1999, pp.242–249.
  4. El Emam, K., Benlarbi, S., Goel, N., & Rai, S. The confounding effect of class size on the validity of object-oriented metrics. IEEE Transactions on Software Engineering, 1999, 27(7), pp. 630–650.
  5. El Emam, K., Benlarbi, S., Goel, N., & Rai, S. A validation of object-oriented metrics. Technical report ERB-1063, NRC, 1999.
  6. Briand, L., Daly, W., & Wust, J. Exploring the relationships between design measures and software quality. Journal of Systems and Software, 2000, 51(3), pp.245–273.
  7. Fioravanti, F., & Nesi, P.A Study on Fault-Proneness Detection of Object-Oriented Systems. Proc. Fifth European Conference Software Maintenance and Reengineering (CSMR 2001), 2001, pp. 121-130.
  8. El Emam, K., W.Melo, and J.Machado. The prediction of faulty classes using object-oriented design metrics. The Journal of Systems and Software, 2001, 56(1),pp.63–75
  9. Yu, P., Systa, T., & Muller, H. Predicting fault-proneness using OO metrics: An industrial case study. In Proceedings of Sixth European Conference on Software Maintenance and Reengineering, Budapest, Hungary, 2002, pp.99–107.
  10. Subramanyam, R. and M.S. Krishnan. Empirical analysis of CK metrics for object oriented design complexity. Implications for software defects. IEEE Trans. Software Eng., vol. 29, 2003, pp. 297-310.
  11. Gyimothy, T., Ferenc, R., & Siket, I. Empirical validation of object-oriented metrics on open source software for fault prediction. IEEE Transactions on Software Engineering, 31(10), 2005, pp.897–910.
  12. Zhou, Y., & Leung, H. Empirical analysis of object oriented design metrics for predicting high severity faults. IEEE Transactions on Software Engineering, 32(10), 2006, pp. 771–784.
  13. Olague, H., Etzkorn, L., Gholston, S., & Quattlebaum, S. Empirical validation of three software metrics suites to predict fault-proneness of object-oriented classes developed using highly iterative or agile software development processes. IEEE Transactions on Software Engineering, 33(8), 2007, pp.402–419.
  14. Singh, Y., Kaur, A., & Malhotra, R. Empirical validation of object-oriented metrics for predicting fault proneness models.SoftwareQualityJournal, 2010, pp.3-35.
  15. Munson, J.C., & Khoshgoftaar, T. The Detection of Fault-Prone Programs.IEEE Trans. on Reliability, 1992, vol.51, pp.423-432.
  16. Chidamber, S., & Kemerer, C. A metrics suite for object-oriented design. IEEE Transactions on Software Engineering, 1994, 20(6), pp.476–493.
  17. Abreu, F. B., "The MOOD Metrics Set," presented at ECOOP '95 Workshop on Metrics, 1995.
  18. Briand, L., Daly, W., & Wust, J.Replicated Case Studies for Investigating Quality Factors in Object-Oriented Designs. Empirical Software Engineering International Journal, 6(1), pp.11-58.
Index Terms

Computer Science
Information Sciences

Keywords

Object Oriented Metrics Fault Proneness

Powered by PhDFocusTM