Commenced in January 2007
Paper Count: 30184
Software Maintenance Severity Prediction for Object Oriented Systems
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 in time especially for the critical applications. As, Neural networks, which have been already applied in software engineering applications to build reliability growth models predict the gross change or reusability metrics. Neural networks are non-linear sophisticated modeling techniques that are able to model complex functions. Neural network techniques are used when exact nature of input and outputs is not known. A key feature is that they learn the relationship between input and output through training. In this present work, various Neural Network Based techniques are explored and comparative analysis is performed for the prediction of level of need of maintenance by predicting level severity of faults present in NASA-s public domain defect dataset. The comparison of different algorithms is made on the basis of Mean Absolute Error, Root Mean Square Error and Accuracy Values. It is concluded that Generalized Regression Networks is the best algorithm for classification of the software components into different level of severity of impact of the faults. The algorithm can be used to develop model that can be used for identifying modules that are heavily affected by the faults.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1070997Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1237
 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.
 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
 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.