Abstract

There has been a tremendous growth in the demand for software fault prediction during recent years. In this paper, Levenberg-Marquardt (LM) algorithm based neural network tool is used for the prediction of software defects at an early stage of the software development life cycle. It helps to minimize the cost of testing which minimizes the cost of the project. The methods, metrics and datasets are used to find the fault proneness of the software. The study used data collected from the PROMISE repository of empirical software engineering data. This dataset uses the CK (Chidamber and Kemerer) OO (object-oriented) metrics. The accuracy of Levenberg-Marquardt (LM) algorithm based neural network are comparing with the polynomial function-based neural network predictors for detection of software defects. Our results indicate that the prediction model has a high accuracy.

References

• Catal, C. , & Diri, B. (2009). A systematic review of software fault prediction studies. Expert Systems with Applications, 36(4), 7346-7354.
• Catal, C. (2011). Software fault prediction: A literature review and current trends. Expert Systems with Applications, 38(4), 4626-4636.
• Gyimothy, T. , Ferenc, R. , & Siket, I. (2005). Empirical validation of object-oriented metrics on open source software for fault prediction. Software Engineering, IEEE Transactions on, 31(10), 897-910.
• El Emam, K. , Melo, W. , & Machado, J. C. (2001). The prediction of faulty classes using object-oriented design metrics. Journal of Systems and Software, 56(1), 63-75.
• Zhao, M. , Wohlin, C. , Ohlsson, N. , & Xie, M. (1998). A comparison between software design and code metrics for the prediction of software fault content. Information and Software Technology, 40(14), 801-809.
• Tomaszewski, P. , Lundberg, L. , & Grahn, H. (2005). The accuracy of early fault prediction in modified code. In Proceedings of the Fifth Conference on Software Engineering Research and Practice in Sweden (SERPS) (pp. 57-63).
• Challagulla, V. U. , Bastani, F. B. , Yen, I. L. , & Paul, R. A. (2005, February). Empirical assessment of machine learning based Software defect prediction techniques. In Object-Oriented Real-Time Dependable Systems, 2005. WORDS 2005. 10th IEEE International Workshop on (pp. 263-270). IEEE. 4
• Singh, Y. , Kaur, A. , & Malhotra, R. (2010). Prediction of fault-prone software modules using statistical and machine learning methods. International Journal of Computer Applications, 1(22), 8-15.
• Tomaszewski, P. , Håkansson, J. , Grahn, H. , & Lundberg, L. (2007). Statistical models vs. expert estimation for fault prediction in modified code–an industrial case study. Journal of Systems and Software, 80(8), 1227-1238.
• Park, B. J. , Oh, S. K. , & Pedrycz, W. (2011). The design of polynomial function-based neural network predictors for detection of software defects. Information Sciences.
• Craven, M. W. , & Shavlik, J. W. (1997). Using neural networks for data mining. Future generation computer systems, 13(2), 211-229.
• Egmont-Petersen, M. , de Ridder, D. , & Handels, H. (2002). Image processing with neural networks—a review. Pattern recognition, 35(10), 2279-2301.
• Reategui, E. B. , Campbell, J. A. , & Leao, B. F. (1997). Combining a neural network with case-based reasoning in a diagnostic system. Artificial Intelligence in Medicine, 9(1), 5.
• Chidamber, S. R. , & Kemerer, C. F. (1994). A metrics suite for object oriented design. Software Engineering, IEEE Transactions on, 20(6), 476-493.
• Sellers, B. H. (1996). Ojbect-Oriented Metrics. Measures of Complexity.
• Bansiya, J. , & Davis, C. G. (2002). A hierarchical model for object-oriented design quality assessment. Software Engineering, IEEE Transactions on, 28(1), 4-17.
• Tang, M. H. , Kao, M. H. , & Chen, M. H. (1999). An empirical study on object-oriented metrics. In Software Metrics Symposium, 1999. Proceedings. Sixth International (pp. 242-249). IEEE.
• Martin, R. (1994). OO design quality metrics. An analysis of dependencies.
• McCabe, T. J. (1976). A complexity measure. Software Engineering, IEEE Transactions on, (4), 308-320.