A Genetic Algorithm Based Classification Approach for Finding Fault Prone Classes
Fault-proneness of a software module is the
probability that the module contains faults. A correlation exists
between the fault-proneness of the software and the measurable
attributes of the code (i.e. the static metrics) and of the testing (i.e.
the dynamic metrics). Early detection of fault-prone software
components enables verification experts to concentrate their time and
resources on the problem areas of the software system under
development. This paper introduces Genetic Algorithm based
software fault prediction models with Object-Oriented metrics. The
contribution of this paper is that it has used Metric values of JEdit
open source software for generation of the rules for the classification
of software modules in the categories of Faulty and non faulty
modules and thereafter empirically validation is performed. The
results shows that Genetic algorithm approach can be used for
finding the fault proneness in object oriented software components.
[1] Koru, H. Liu, "Building effective defect- prediction models in practice",
IEEE Software, 2005, pp.23-29.
[2] S. Chidamber, and C. Kemerer, "A metrics suite for object-oriented
design", IEEE Transactions on Software Engineering, 20(6), 1994,
pp.476-493.
[3] Arvinder Kaur and Ruchika Malhotra, "Application of Random Forest in
Predicting Fault-Prone Classes", 2008 International Conference on
Advanced Computer Theory and Engineering ICACTE 2008, Pukhet,
Dec. 2008, pp. 37-43.
[4] Lanubile F., Lonigro A., and Visaggio G. (1995) "Comparing Models
for Identifying Fault-Prone Software Components", Proceedings of
Seventh International Conference on Software Engineering and
Knowledge Engineering, June 1995, pp. 12-19.
[5] Saida Benlarbi,Khaled El Emam, Nishith Geol (1999), "Issues in
Validating Object-Oriented Metrics for Early Risk Prediction", by Cistel
Technology 210 Colonnade Road Suite 204 Nepean, Ontario Canada
K2E 7L5.
[6] Runeson, Claes Wohlin and Magnus C. Ohlsson (2001), "A Proposal for
Comparison of Models for Identification of Fault-Proneness", Dept. of
Communication Systems, Lund University, Profes 2001, LNLS 2188,
pp. 341-355.
[7] Mahaweerawat, A. (2004), "Fault-Prediction in object oriented
software-s using neural network techniques", Advanced Virtual and
Intelligent Computing Center (AVIC), Department of Mathematics,
Faculty of Science, Chulalongkorn University, Bangkok, Thailand, pp.
1-8.
[8] Bellini, P. (2005), "Comparing Fault-Proneness Estimation Models",
10th IEEE International Conference on Engineering of Complex
Computer Systems (ICECCS'05), vol. 0, 2005, pp. 205-214.
[9] Ma, Y., Guo, L. (2006), "A Statistical Framework for the Prediction of
Fault-Proneness", West Virginia University, Morgantown.
[10] Eric Rotenberg (1999), "AR-SMT: A Microarchitectural Approach to
Fault Tolerance in Microprocessors", Proceedings of the Twenty-Ninth
Annual International Symposium on Fault-Tolerant Computing, June 15-
18, pp. 84-90.
[11] L. Briand, J. Wilst, H. Lounis, "Replicated Case Studies for
Investigating Quality Factors in Object-Oriented Designs", Empirical
Software Engineering: An International Journal, 6(1), 2001, pp.11-58.
[12] T. Gyimothy, R. Ferenc, 1. Siket, "Empirical validation of objectoriented
metrics on open Trans. Software Engineering, 31 (10), 2005,
pp. 897 ÔÇö910.
[13] Z. Yuming, and L. Hareton, "Empirical analysis of Object-Oriented
Design Metrics for predicting high severity faults", IEEE Transactions
on Software Engineering, 32(10), 2006, pp.771-784.
[14] G. Pai, "Empirical analysis of Software Fault Content and Fault
Proneness Using Bayesian Methods", IEEE Transactions on software
Engineering, 33(10), 2007, pp.675-686.
[15] K.K Aggarwal, Y. Singh, A. Kaur, R. Malhotra, "Empirical Analysis for
Investigating the Effect of Object-Oriented Metrics on Fault Proneness:
A Replicated Case Study", Published online in Software Process
Improvement and Practice, Wiley, 2008.
[16] K.K Aggarwal, Y. Singh, A. Kaur, R. Malhotra, "Investigating the Effect
of Coupling Metrics on Fault Proneness in Object-Oriented Systems",
Software Quality Professional, 8(4), 2006, pp.4-16.
[17] T.M. Khoshgaftaar, E.D. Allen, J.P. Hudepohl, S.J. Aud, Application of
neural networks to software quality modeling of a very large
telecommunications system, IEEE Transactions on Neural Networks,
8(4), 1997, pp. 902-909.
[18] Promise. http://promisedata.org/repository/.
[19] Website sourceforge: www.sourceforge.net/projects/jedit
[20] S. Watanabe, H. Kaiya, K. Kaijiri, Adapting a Fault Prediction Model to
Allow Inter Language Reuse, PROMISE'08, May 12-13, Leipzig,
Germany, 2008.
[1] Koru, H. Liu, "Building effective defect- prediction models in practice",
IEEE Software, 2005, pp.23-29.
[2] S. Chidamber, and C. Kemerer, "A metrics suite for object-oriented
design", IEEE Transactions on Software Engineering, 20(6), 1994,
pp.476-493.
[3] Arvinder Kaur and Ruchika Malhotra, "Application of Random Forest in
Predicting Fault-Prone Classes", 2008 International Conference on
Advanced Computer Theory and Engineering ICACTE 2008, Pukhet,
Dec. 2008, pp. 37-43.
[4] Lanubile F., Lonigro A., and Visaggio G. (1995) "Comparing Models
for Identifying Fault-Prone Software Components", Proceedings of
Seventh International Conference on Software Engineering and
Knowledge Engineering, June 1995, pp. 12-19.
[5] Saida Benlarbi,Khaled El Emam, Nishith Geol (1999), "Issues in
Validating Object-Oriented Metrics for Early Risk Prediction", by Cistel
Technology 210 Colonnade Road Suite 204 Nepean, Ontario Canada
K2E 7L5.
[6] Runeson, Claes Wohlin and Magnus C. Ohlsson (2001), "A Proposal for
Comparison of Models for Identification of Fault-Proneness", Dept. of
Communication Systems, Lund University, Profes 2001, LNLS 2188,
pp. 341-355.
[7] Mahaweerawat, A. (2004), "Fault-Prediction in object oriented
software-s using neural network techniques", Advanced Virtual and
Intelligent Computing Center (AVIC), Department of Mathematics,
Faculty of Science, Chulalongkorn University, Bangkok, Thailand, pp.
1-8.
[8] Bellini, P. (2005), "Comparing Fault-Proneness Estimation Models",
10th IEEE International Conference on Engineering of Complex
Computer Systems (ICECCS'05), vol. 0, 2005, pp. 205-214.
[9] Ma, Y., Guo, L. (2006), "A Statistical Framework for the Prediction of
Fault-Proneness", West Virginia University, Morgantown.
[10] Eric Rotenberg (1999), "AR-SMT: A Microarchitectural Approach to
Fault Tolerance in Microprocessors", Proceedings of the Twenty-Ninth
Annual International Symposium on Fault-Tolerant Computing, June 15-
18, pp. 84-90.
[11] L. Briand, J. Wilst, H. Lounis, "Replicated Case Studies for
Investigating Quality Factors in Object-Oriented Designs", Empirical
Software Engineering: An International Journal, 6(1), 2001, pp.11-58.
[12] T. Gyimothy, R. Ferenc, 1. Siket, "Empirical validation of objectoriented
metrics on open Trans. Software Engineering, 31 (10), 2005,
pp. 897 ÔÇö910.
[13] Z. Yuming, and L. Hareton, "Empirical analysis of Object-Oriented
Design Metrics for predicting high severity faults", IEEE Transactions
on Software Engineering, 32(10), 2006, pp.771-784.
[14] G. Pai, "Empirical analysis of Software Fault Content and Fault
Proneness Using Bayesian Methods", IEEE Transactions on software
Engineering, 33(10), 2007, pp.675-686.
[15] K.K Aggarwal, Y. Singh, A. Kaur, R. Malhotra, "Empirical Analysis for
Investigating the Effect of Object-Oriented Metrics on Fault Proneness:
A Replicated Case Study", Published online in Software Process
Improvement and Practice, Wiley, 2008.
[16] K.K Aggarwal, Y. Singh, A. Kaur, R. Malhotra, "Investigating the Effect
of Coupling Metrics on Fault Proneness in Object-Oriented Systems",
Software Quality Professional, 8(4), 2006, pp.4-16.
[17] T.M. Khoshgaftaar, E.D. Allen, J.P. Hudepohl, S.J. Aud, Application of
neural networks to software quality modeling of a very large
telecommunications system, IEEE Transactions on Neural Networks,
8(4), 1997, pp. 902-909.
[18] Promise. http://promisedata.org/repository/.
[19] Website sourceforge: www.sourceforge.net/projects/jedit
[20] S. Watanabe, H. Kaiya, K. Kaijiri, Adapting a Fault Prediction Model to
Allow Inter Language Reuse, PROMISE'08, May 12-13, Leipzig,
Germany, 2008.
@article{"International Journal of Information, Control and Computer Sciences:63455", author = "Parvinder S. Sandhu and Satish Kumar Dhiman and Anmol Goyal", title = "A Genetic Algorithm Based Classification Approach for Finding Fault Prone Classes", abstract = "Fault-proneness of a software module is the
probability that the module contains faults. A correlation exists
between the fault-proneness of the software and the measurable
attributes of the code (i.e. the static metrics) and of the testing (i.e.
the dynamic metrics). Early detection of fault-prone software
components enables verification experts to concentrate their time and
resources on the problem areas of the software system under
development. This paper introduces Genetic Algorithm based
software fault prediction models with Object-Oriented metrics. The
contribution of this paper is that it has used Metric values of JEdit
open source software for generation of the rules for the classification
of software modules in the categories of Faulty and non faulty
modules and thereafter empirically validation is performed. The
results shows that Genetic algorithm approach can be used for
finding the fault proneness in object oriented software components.", keywords = "Genetic Algorithms, Software Fault, Classification,Object Oriented Metrics.", volume = "3", number = "12", pages = "2935-4", }
