research-article

Application of support vector machine to predict fault prone classes

Authors:

Ruchika MalhotraAuthors Info & Claims

ACM SIGSOFT Software Engineering Notes, Volume 34, Issue 1

Pages 1 - 6

https://doi.org/10.1145/1457516.1457529

Published: 31 January 2009 Publication History

Abstract

Empirical validation of software metrics to predict quality using machine learning methods is important to ensure their practical relevance in the software organizations. It would also be interesting to know the relationship between object-oriented metrics and fault proneness. In this paper, we build a Support Vector Machine (SVM) model to find the relation-ship between object-oriented metrics given by Chidamber and Kemerer and fault proneness. The proposed model is empirically evaluated using open source software. The performance of the SVM method was evaluated by Receiver Operating Characteristic (ROC) analysis. Based on these results, it is reasonable to claim that such models could help for planning and performing testing by focusing resources on fault- prone parts of the design and code. Thus, the study shows that SVM method may also be used in constructing software quality models. However, similar types of studies are required to be carried out in order to establish the acceptability of the model.

References

[1]

Aggarwal K.K., Singh Y., Kaur A., Malhotra R. (2005): Analysis of Object-Oriented Metrics. In Proceedings of the International Workshop on Software Measurement (IWSM), Montral, Canada.

[2]

Aggarwal K.K., Singh Y., Kaur A., Malhotra R. (2008): Empirical Analysis for Investigating the Effect of Object-Oriented Metrics on Fault Proneness: A Replicated Case Study, Forthcoming in Software Process Improvement and Practice, Wiley.

Digital Library

[3]

Aggarwal K.K., Singh Y., Kaur A., Malhotra R. (2006): Empirical study of object-oriented metrics. Journal of Object Technology, 5(8), pp.149--173.

[4]

Aggarwal K.K., Singh Y., Kaur A., Malhotra R. (2005): Software reuse metrics for object-oriented systems. In Proceedings of the Third ACIS Intl Conference on Software Engineering Research, Management and Applications (SERA 05), pp.48--55.

Digital Library

[5]

Aggarwal K.K., Singh Y., Kaur A., Malhotra R. (2006): Investigating the Effect of Coupling Metrics on Fault Proneness in Object-Oriented Systems. Software Quality Professional, 8(4), pp.4--16.

[6]

Basili,V., Briand, L., and Melo, W. (1996): A validation of object-oriented design metrics as quality indicators. IEEE Transactions on Software Engineering, 22(10), pp.751--761.

Digital Library

[7]

Bieman, J., and Kang, B. (1995): Cohesion and reuse in an object-oriented system. In Proceedings of the ACM Symposium on Software Reusability (SSR94), pp.259--262.

Digital Library

[8]

Binkley, A., and Schach, S. (1998): Validation of the coupling dependency metric as a risk predictor. In Proceedings of the International Conference on Software Engineering, pp.452--455.

Digital Library

[9]

Briand, L., Daly, W., and Wust J. (1998): Unified framework for cohesion measurement in object-oriented systems. Empirical Software Engineering, 3(1), pp.65--117.

Digital Library

[10]

Briand, L., Daly, W., and Wust J. (1999): A unified framework for coupling measurement in object-oriented systems. IEEE Transactions on Software Engineering, 25(1), pp.91--121.

Digital Library

[11]

Briand, L., Daly, W., and Wust J. (2000): Exploring the relationships between design measures and software quality. Journal of Systems and Software, 51(3), pp.245--273.

Digital Library

[12]

Briand L., Wst J., Lounis H. (2001): Replicated Case Studies for Investigating Quality Factors in Object-Oriented Designs, Empirical Software Engineering: An International Journal, 6(1), pp.11--58.

Digital Library

[13]

Burges, C. (1998): A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2, pp.121--167.

Digital Library

[14]

Cartwright, M., and Shepperd, M. (1999): An empirical investigation of an object-oriented software system. IEEE Transactions of Software Engineering, 26(8), pp.786--796.

Digital Library

[15]

Chidamber, S., and Kamerer, C. (1994): A metrics suite for object-oriented design. IEEE Transactions on Software Engineering, 20(6), pp.476--493.

Digital Library

[16]

Chidamber, S., and Kamerer, C. (1991): Towards a metrics suite for object oriented design. In Proceedings of the Conference on Object-Oriented Programming: Systems, Languages and Applications (OOPSLA91). SIGPLAN Notices, 26(11), pp.197--211.

Digital Library

[17]

Chidamber, S., Darcy, D., and Kemerer, C. (1998): Managerial use of metrics for object-oriented software: An exploratory analysis. IEEE Transactions on Software Engineering, 24(8), pp.629--639.

Digital Library

[18]

Cortes, C., Vapnik, V. (1995): Support-vector networks. Machine Learning 20, pp.273--297.

[19]

Cristianini, N., Shawe-Taylor, J. (2000): An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press, Cambridge, UK.

Digital Library

[20]

El Emam, K., Benlarbi, S., Goel, N. And Rai, S. 1999: A Validation of Object-Oriented Metrics, Technical Report ERB-1063, NRC.

[21]

El Emam, K., Benlarbi, S., Goel, N., and Rai, S. (2001): The Confounding Effect of Class Size on The Validity of Object-Oriented Metrics. IEEE Transactions on Software Engineering, 27(7), pp.630--650.

Digital Library

[22]

Gyimothy, T., Ferenc, R., Siket, I. (2005): Empirical validation of object-oriented metrics on open source software for fault prediction, IEEE Trans. Software Engineering, 31 (10), pp.897--910.

Digital Library

[23]

Hanley, J., McNeil, BJ. (1982): The meaning and use of the area under a Receiver Operating Characteristic ROC curve. Radiology, 143, pp.29--36.

[24]

Harrison, R., Counsell, S. J., and Nithi, R.V. (1998): An evaluation ofMOOD set of object-oriented software metrics. IEEE Transactions on Software Engineering, 24(6), pp.491--496.

Digital Library

[25]

Henderson-Sellers, B. (1996): Object-oriented metrics, measures of complexity. Englewood Cliffs, N.J.: Prentice Hall.

Digital Library

[26]

Hitz, M., and Montazeri, B. (1995): Measuring coupling and cohesion in object-oriented systems. In Proceedings of the International Symposium on Applied Corporate Computing, Monterrey, Mexico.

[27]

Jedit. http://sourceforge.net/projects/jedit/.

[28]

Lake, A., and Cook, C. (1994): Use of factor analysis to develop OOP software complexity metrics. In Proceedings of the 6th Annual Oregon Workshop on Software Metrics, Silver Falls, Oregon.

[29]

Lee, Y., Liang, B., Wu, S., and Wang, F. (1995): Measuring the coupling and cohesion of an object-oriented program based on information flow. In Proceedings of the International Conference on Software Quality, Maribor, Slovenia.

[30]

Li, W., and Henry, S. (1993): Object-oriented metrics that predict maintainability. Journal of Systems and Software, 23(2), 111--122.

Digital Library

[31]

Lorenz, M., and Kidd, J. (1994): Object-oriented software metrics. Englewood Cliffs, N.J.: Prentice-Hall.

Digital Library

[32]

Morris, C., Autret, A., Boddy, L. (2001): Support vector machines for identifying organisms-a comparison with strongly partitioned radial basis function networks. Ecological Modeling 146, pp.57--67.

[33]

Olague, H., Etzkorn, L., Gholston, S., and Quattlebaum, S. (2007): 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), pp.402--419.

Digital Library

[34]

Pai, G. (2007): Empirical analysis of Software Fault Content and Fault Proneness Using Bayesian Methods, IEEE Transactions on software Engineering, 33(10), pp.675--686.

Digital Library

[35]

Promise. http://promisedata.org/repository/.

[36]

Scitools. http://www.scitools.com/index.php.

[37]

Sherrod, P. (2003): DTreg Predictive Modeling Software.

[38]

Stone, M. (1974): Cross-validatory choice and assessment of statistical predictions. J. Royal Stat. Soc., 36, pp.111--147.

[39]

Tang, M.H, Kao, M.H., and Chen, M.H. (1999): An Empirical Study on Object-Oriented Metrics, In Proceedings of Metrics, pp.242--249.

Digital Library

[40]

Tegarden, D., Sheetz, S., and Monarchi, D. (1995): A software complexity model of object-oriented systems. Decision Support Systems 13 (3-4), pp.241--262.

Digital Library

[41]

Wang, X., Bi, D., and Wang, S. (2007): Fault recognition with Labeled multi-category, Third conference on Natural Computation, Haikou, China.

Digital Library

[42]

Watanabe, S., Kaiya, H., Kaijiri, K. (2008): Adapting a Fault Prediction Model to Allow Inter Language Reuse, PROMISE08, May 1213, Leipzig,Germany.

Digital Library

[43]

Yuming, Z. and Hareton, L. (2006): Empirical analysis of Object-Oriented Design Metrics for predicting high severity faults. IEEE Transactions on Software Engineering, 32(10), pp.771--784.

Digital Library

[44]

Zhao, L., Takagi, N. (2007): An application of Support vector machines to Chinese character classification problem. IEEE International Conference on systems, Man and Cybernetics, Montreal.

Cited By

Malhotra RKashyap VSharma V(2022)Comparative study of Sampling Techniques for Software Defect Prediction2022 8th International Conference on Advanced Computing and Communication Systems (ICACCS)10.1109/ICACCS54159.2022.9785238(760-766)Online publication date: 25-Mar-2022
https://doi.org/10.1109/ICACCS54159.2022.9785238
Malhotra RAgrawal VPal VAgarwal T(2021)Support Vector based Oversampling Technique for Handling Class Imbalance in Software Defect Prediction2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence)10.1109/Confluence51648.2021.9377068(1078-1083)Online publication date: 28-Jan-2021
https://doi.org/10.1109/Confluence51648.2021.9377068
Kumar PWahid A(2020)Investigation of Software Reliability Prediction Using Statistical and Machine Learning MethodsCognitive Analytics10.4018/978-1-7998-2460-2.ch085(1640-1660)Online publication date: 2020
https://doi.org/10.4018/978-1-7998-2460-2.ch085
Show More Cited By

Index Terms

Application of support vector machine to predict fault prone classes

Recommendations

Twin support vector machine: theory, algorithm and applications

Twin support vector machine (TWSVM) has gained increasing interest from various research fields recently. In this paper, we aim to report the current state of the theoretical research and practical advances on TWSVM. We first give the basic thought and ...
On the ability of complexity metrics to predict fault-prone classes in object-oriented systems

Many studies use logistic regression models to investigate the ability of complexity metrics to predict fault-prone classes. However, it is not uncommon to see the inappropriate use of performance indictors such as odds ratio in previous studies. In ...
Predicting fault-prone modules based on metrics transitions
DEFECTS '08: Proceedings of the 2008 workshop on Defects in large software systems

This paper describe a method for identifying fault-prone modules. The method utilizes metrics transitions rather than raw metrics values. Metrics transitions are measured from the source code of consecutive versions, which is archived in software ...

Comments

Information & Contributors

Information

Published In

cover image ACM SIGSOFT Software Engineering Notes

ACM SIGSOFT Software Engineering Notes Volume 34, Issue 1

January 2009

119 pages

ISSN:0163-5948

DOI:10.1145/1457516

Issue’s Table of Contents

Copyright © 2009 Authors.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 31 January 2009

Published in SIGSOFT Volume 34, Issue 1

Check for updates

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

14
Total Citations
View Citations
418
Total Downloads

Downloads (Last 12 months)3
Downloads (Last 6 weeks)0

Reflects downloads up to 03 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Malhotra RKashyap VSharma V(2022)Comparative study of Sampling Techniques for Software Defect Prediction2022 8th International Conference on Advanced Computing and Communication Systems (ICACCS)10.1109/ICACCS54159.2022.9785238(760-766)Online publication date: 25-Mar-2022
https://doi.org/10.1109/ICACCS54159.2022.9785238
Malhotra RAgrawal VPal VAgarwal T(2021)Support Vector based Oversampling Technique for Handling Class Imbalance in Software Defect Prediction2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence)10.1109/Confluence51648.2021.9377068(1078-1083)Online publication date: 28-Jan-2021
https://doi.org/10.1109/Confluence51648.2021.9377068
Kumar PWahid A(2020)Investigation of Software Reliability Prediction Using Statistical and Machine Learning MethodsCognitive Analytics10.4018/978-1-7998-2460-2.ch085(1640-1660)Online publication date: 2020
https://doi.org/10.4018/978-1-7998-2460-2.ch085
Sefid-Dashti BHabibi J(2018)A Reference Architecture for Mobile SOASystems Engineering10.1111/sys.2127917:4(407-425)Online publication date: 17-Dec-2018
https://dl.acm.org/doi/10.1111/sys.21279
Kumar P(2017)ConclusionUbiquitous Machine Learning and Its Applications10.4018/978-1-5225-2545-5.ch011(217-229)Online publication date: 2017
https://doi.org/10.4018/978-1-5225-2545-5.ch011
Kumar P(2017)Application of Machine Learning Techniques for Software Reliability Prediction (SRP)Ubiquitous Machine Learning and Its Applications10.4018/978-1-5225-2545-5.ch006(113-142)Online publication date: 2017
https://doi.org/10.4018/978-1-5225-2545-5.ch006
Kumar PWahid A(2017)Investigation of Software Reliability Prediction Using Statistical and Machine Learning MethodsHandbook of Research on Machine Learning Innovations and Trends10.4018/978-1-5225-2229-4.ch012(251-271)Online publication date: 2017
https://doi.org/10.4018/978-1-5225-2229-4.ch012
Zhang WHuang LNg VGe J(2015)SMPLearnerAutomated Software Engineering10.1007/s10515-014-0161-322:1(111-141)Online publication date: 1-Mar-2015
https://dl.acm.org/doi/10.1007/s10515-014-0161-3
Malhotra RRaje R(2014)An empirical comparison of machine learning techniques for software defect predictionProceedings of the 8th International Conference on Bioinspired Information and Communications Technologies10.4108/icst.bict.2014.257871(320-327)Online publication date: 1-Dec-2014
https://dl.acm.org/doi/10.4108/icst.bict.2014.257871
Treiber MSchall DDustdar SScherling COssowski SLecca P(2012)Creating mobile ad hoc workflows with TwitterProceedings of the 27th Annual ACM Symposium on Applied Computing10.1145/2245276.2232107(1998-2000)Online publication date: 26-Mar-2012
https://dl.acm.org/doi/10.1145/2245276.2232107
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Issue’s Table of Contents