Software Maintenance Severity Prediction with Soft Computing Approach
As the majority of faults are found in a few of its modules so there is a need to investigate the modules that are affected severely as compared to other modules and proper maintenance need to be done on time especially for the critical applications. In this paper, we have explored the different predictor models to NASA-s public domain defect dataset coded in Perl programming language. Different machine learning algorithms belonging to the different learner categories of the WEKA project including Mamdani Based Fuzzy Inference System and Neuro-fuzzy based system have been evaluated for the modeling of maintenance severity or impact of fault severity. The results are recorded in terms of Accuracy, Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The results show that Neuro-fuzzy based model provides relatively better prediction accuracy as compared to other models and hence, can be used for the maintenance severity prediction of the software.
[1] 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.
[2] 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.
[3] Fenton, N. E. and Neil, M. (1999), "A Critique of Software Defect
Prediction Models", Bellini, I. Bruno, P. Nesi, D. Rogai, University of
Florence, IEEE Trans. Softw. Engineering, vol. 25, Issue no. 5, pp. 675-
689.
[4] Giovanni Denaro (2000), "Estimating Software Fault-Proneness for
Tuning Testing Activities" Proceedings of the 22nd International
Conference on Software Engineering (ICSE2000), Limerick, Ireland,
June 2000.
[5] Manasi Deodhar (2002), "Prediction Model and the Size Factor for
Fault-proneness of Object Oriented Systems", MS Thesis, Michigan
Tech. University, Dec. 2002.
[6] 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.
[7] Khoshgoftaar, T.M., K. Gao and R. M. Szabo ( 2001), "An Application
of Zero-Inflated Poisson Regression for Software Fault Prediction.
Software Reliability Engineering", ISSRE 2001. Proceedings of 12th
International Symposium on, 27-30 Nov. (2001), pp: 66 -73.
[8] Munson, J. and T. Khoshgoftaar, (1990) "Regression Modeling of
Software Quality: An Empirical Investigation", Information and
Software Technology, 32(2): 106 - 114.
[9] Khoshgoftaar, T. M. and J. C. Munson, (1990). "Predicting Software
Development Errors using Complexity Metrics", IEEE Journal on
Selected Areas in Communications, 8(2): 253 -261.
[10] Menzies, T., K. Ammar, A. Nikora, and S. Stefano, (2003), "How
Simple is Software Defect Prediction?", Journal of Empirical Software
Engineering, October (2003).
[11] Eman, K., S. Benlarbi, N. Goel and S. Rai, (2001), "Comparing casebased
reasoning classifiers for predicting high risk software
components", Journal of Systems Software, 55(3): 301 - 310.
[12] Hudepohl, J. P., S. J. Aud, T. M. Khoshgoftaar, E. B. Allen, and J. E.
Mayrand, (1996), "Software Metrics and Models on the Desktop", IEEE
Software, 13(5): 56-60.
[13] Khoshgoftaar, T. M., E. B. Allen, K. S. Kalaichelvan, and N. Goel,
(1996), "Early quality prediction: a case study in telecommunications",
IEEE Software (1996), 13(1): 65-71.
[14] Khoshgoftaar, T. M. and N. Seliya, (2002), "Tree-based software quality
estimation models for fault prediction", METRICS 2002, the Eighth IIIE
Symposium on Software Metrics, pp: 203-214.
[15] Seliya N., T. M. Khoshgoftaar, S. Zhong, (2005), "Analyzing software
quality with limited fault-proneness defect data", Ninth IEEE
international Symposium, Oct 12-14, (2005).
[16] Munson, J. C. and T. M. Khoshgoftaar, (1992), "The detection of faultprone
programs", IEEE Transactions on Software Engineering, 18(5):
423-433.
[17] Sandhu, Parvinder Singh, Sunil Kumar and Hardeep Singh, (2007),
"Intelligence System for Software Maintenance Severity Prediction",
Journal of Computer Science, Vol. 3 (5), pp. 281-288, 2007
[18] Abraham A., (2005), "Hybrid Intelligent Systems: Evolving Intelligence
in Hierarchical Layers", Studies in Fuzziness and Soft Computing, vol.
173, 2005, pp. 159-179.
[19] Yen J. and Langari R. (2003), "Fuzzy Logic: Intelligence, Control, and
Information" Pearson Education.
[20] J-S. R. Jang and C.T. Sun, (1995), "Neuro-fuzzy Modeling and
Control", Proceeding of the IEEE, March 1995.
[21] Challagulla, V.U.B., Bastani, F.B., I-Ling Yen, Paul, (2005), "Empirical
assessment of machine learning based software defect prediction
techniques", 10th IEEE International Workshop on Object-Oriented
Real-Time Dependable Systems, WORDS 2005, 2-4 Feb 2005, pp. 263-
270.
[22] www.cs.waikato.ac.nz/~ml/weka/.
[23] Abraham, Ajith, (2001), "Neuro-Fuzzy Systems: State-of-the-Art
Modeling Techniques, Connectionist Models of Neurons, Learning
Processes, and Artificial Intelligence", Mira Jose, Prieto Alberto ed.,
Lecture Notes in Computer Science, vol. 2084. Germany: Springer-
Verlag; 2001, pp. 269-276.
[24] Jang, J.-S. R., Sun, C.-T. and Mizutani, E., (2004), "Neuro-Fuzzy and
Soft Computing- A Computational Approach to Learning and Machine
Intelligence", Pearson Education (Singapore) Pvt. Ltd., 1st Edition,
2004.
[1] 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.
[2] 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.
[3] Fenton, N. E. and Neil, M. (1999), "A Critique of Software Defect
Prediction Models", Bellini, I. Bruno, P. Nesi, D. Rogai, University of
Florence, IEEE Trans. Softw. Engineering, vol. 25, Issue no. 5, pp. 675-
689.
[4] Giovanni Denaro (2000), "Estimating Software Fault-Proneness for
Tuning Testing Activities" Proceedings of the 22nd International
Conference on Software Engineering (ICSE2000), Limerick, Ireland,
June 2000.
[5] Manasi Deodhar (2002), "Prediction Model and the Size Factor for
Fault-proneness of Object Oriented Systems", MS Thesis, Michigan
Tech. University, Dec. 2002.
[6] 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.
[7] Khoshgoftaar, T.M., K. Gao and R. M. Szabo ( 2001), "An Application
of Zero-Inflated Poisson Regression for Software Fault Prediction.
Software Reliability Engineering", ISSRE 2001. Proceedings of 12th
International Symposium on, 27-30 Nov. (2001), pp: 66 -73.
[8] Munson, J. and T. Khoshgoftaar, (1990) "Regression Modeling of
Software Quality: An Empirical Investigation", Information and
Software Technology, 32(2): 106 - 114.
[9] Khoshgoftaar, T. M. and J. C. Munson, (1990). "Predicting Software
Development Errors using Complexity Metrics", IEEE Journal on
Selected Areas in Communications, 8(2): 253 -261.
[10] Menzies, T., K. Ammar, A. Nikora, and S. Stefano, (2003), "How
Simple is Software Defect Prediction?", Journal of Empirical Software
Engineering, October (2003).
[11] Eman, K., S. Benlarbi, N. Goel and S. Rai, (2001), "Comparing casebased
reasoning classifiers for predicting high risk software
components", Journal of Systems Software, 55(3): 301 - 310.
[12] Hudepohl, J. P., S. J. Aud, T. M. Khoshgoftaar, E. B. Allen, and J. E.
Mayrand, (1996), "Software Metrics and Models on the Desktop", IEEE
Software, 13(5): 56-60.
[13] Khoshgoftaar, T. M., E. B. Allen, K. S. Kalaichelvan, and N. Goel,
(1996), "Early quality prediction: a case study in telecommunications",
IEEE Software (1996), 13(1): 65-71.
[14] Khoshgoftaar, T. M. and N. Seliya, (2002), "Tree-based software quality
estimation models for fault prediction", METRICS 2002, the Eighth IIIE
Symposium on Software Metrics, pp: 203-214.
[15] Seliya N., T. M. Khoshgoftaar, S. Zhong, (2005), "Analyzing software
quality with limited fault-proneness defect data", Ninth IEEE
international Symposium, Oct 12-14, (2005).
[16] Munson, J. C. and T. M. Khoshgoftaar, (1992), "The detection of faultprone
programs", IEEE Transactions on Software Engineering, 18(5):
423-433.
[17] Sandhu, Parvinder Singh, Sunil Kumar and Hardeep Singh, (2007),
"Intelligence System for Software Maintenance Severity Prediction",
Journal of Computer Science, Vol. 3 (5), pp. 281-288, 2007
[18] Abraham A., (2005), "Hybrid Intelligent Systems: Evolving Intelligence
in Hierarchical Layers", Studies in Fuzziness and Soft Computing, vol.
173, 2005, pp. 159-179.
[19] Yen J. and Langari R. (2003), "Fuzzy Logic: Intelligence, Control, and
Information" Pearson Education.
[20] J-S. R. Jang and C.T. Sun, (1995), "Neuro-fuzzy Modeling and
Control", Proceeding of the IEEE, March 1995.
[21] Challagulla, V.U.B., Bastani, F.B., I-Ling Yen, Paul, (2005), "Empirical
assessment of machine learning based software defect prediction
techniques", 10th IEEE International Workshop on Object-Oriented
Real-Time Dependable Systems, WORDS 2005, 2-4 Feb 2005, pp. 263-
270.
[22] www.cs.waikato.ac.nz/~ml/weka/.
[23] Abraham, Ajith, (2001), "Neuro-Fuzzy Systems: State-of-the-Art
Modeling Techniques, Connectionist Models of Neurons, Learning
Processes, and Artificial Intelligence", Mira Jose, Prieto Alberto ed.,
Lecture Notes in Computer Science, vol. 2084. Germany: Springer-
Verlag; 2001, pp. 269-276.
[24] Jang, J.-S. R., Sun, C.-T. and Mizutani, E., (2004), "Neuro-Fuzzy and
Soft Computing- A Computational Approach to Learning and Machine
Intelligence", Pearson Education (Singapore) Pvt. Ltd., 1st Edition,
2004.
@article{"International Journal of Information, Control and Computer Sciences:52528", author = "E. Ardil and Erdem Uçar and Parvinder S. Sandhu", title = "Software Maintenance Severity Prediction with Soft Computing Approach", abstract = "As the majority of faults are found in a few of its modules so there is a need to investigate the modules that are affected severely as compared to other modules and proper maintenance need to be done on time especially for the critical applications. In this paper, we have explored the different predictor models to NASA-s public domain defect dataset coded in Perl programming language. Different machine learning algorithms belonging to the different learner categories of the WEKA project including Mamdani Based Fuzzy Inference System and Neuro-fuzzy based system have been evaluated for the modeling of maintenance severity or impact of fault severity. The results are recorded in terms of Accuracy, Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The results show that Neuro-fuzzy based model provides relatively better prediction accuracy as compared to other models and hence, can be used for the maintenance severity prediction of the software.", keywords = "Software Metrics, Fuzzy, Neuro-Fuzzy, SoftwareFaults, Accuracy, MAE, RMSE.", volume = "3", number = "2", pages = "312-6", }
