Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30184
Software Maintenance Severity Prediction with Soft Computing Approach

Authors: E. Ardil, Erdem Uçar, Parvinder S. Sandhu

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.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1330267

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1258

References:


[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.