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Predicting the Impact of the Defect on the Overall Environment in Function Based Systems
Abstract:There is lot of work done in prediction of the fault proneness of the software systems. But, it is the severity of the faults that is more important than number of faults existing in the developed system as the major faults matters most for a developer and those major faults needs immediate attention. In this paper, we tried to predict the level of impact of the existing faults in software systems. Neuro-Fuzzy based predictor models is applied NASA-s public domain defect dataset coded in C programming language. As Correlation-based Feature Selection (CFS) evaluates the worth of a subset of attributes by considering the individual predictive ability of each feature along with the degree of redundancy between them. So, CFS is used for the selecting the best metrics that have highly correlated with level of severity of faults. The results are compared with the prediction results of Logistic Models (LMT) that was earlier quoted as the best technique in . 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 provide a relatively better prediction accuracy as compared to other models and hence, can be used for the modeling of the level of impact of faults in function based systems.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1069973Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1215
 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.
 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.
 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.
 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.
 Manasi Deodhar (2002), "Prediction Model and the Size Factor for Fault-proneness of Object Oriented Systems", MS Thesis, Michigan Tech. University, Dec. 2002.
 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.
 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.
 Munson, J. and T. Khoshgoftaar, (1990) "Regression Modeling of Software Quality: An Empirical Investigation", Information and Software Technology, 32(2): 106 - 114.
 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.
 Menzies, T., K. Ammar, A. Nikora, and S. Stefano, (2003), "How Simple is Software Defect Prediction?", Journal of Empirical Software Engineering, October (2003).
 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.
 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.
 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.
 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.
 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).
 Munson, J. C. and T. M. Khoshgoftaar, (1992), "The detection of faultprone programs", IEEE Transactions on Software Engineering, 18(5): 423-433.
 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
 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.
 M. Hall, "Correlation-based feature selection for discrete and numeric class machine learning", In proceedings of the 17th International conference on Machine learning, 2000, pp. 359-366.
 Kaur, A. Malhotra, R., "Application of Random Forest in Predicting Fault-Prone Classes", ICACTE '08. International Conference on Advanced Computer Theory and Engineering, 2008, Phuket, Dec. 20-22, 2008, pp. 37-43
 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.