The Enriched Object Oriented Software processes for Software Fault Prediction

T Ravi Kumar, T Srinivasa Rao


A software fault prediction is a demonstrated strategy in accomplishing high software unwavering quality. Prediction of fault-inclined modules gives one approach to help software quality designing through enhanced booking and venture control. Quality of software is progressively imperative and testing related issues are getting to be noticeably pivotal for software. This requires the need to build up a constant evaluation procedure that groups these progressively created frameworks as being faulty/sans fault. An assortment of software fault predictions procedures have been proposed, In fact different methodologies created by the numerous researchers, they may not be optimal while predication of faults. In this approach we are presenting the fault prediction approach with OO metrics alongside cyclomatic complexity and nested block depth, in acceptance testing, each capacity determined in the plan report can be freely tried, that is, an arrangement of experiments is produced for each capacity, not for every work process module or other module/segment. Our test results demonstrate the productive fault prediction with our algorithm parameters. Our approach predominantly focuses on the tally of faults before testing, expected number of faults, our classification which includes algorithmic and handling, control, rationale and succession, typographical Syntax blunders i.e. off base spelling of a variable name, customary cycle of articulations, off base instatement proclamations per module, this proposed classification approach demonstrates optimal results while analyzing the metrics with preparing tests after estimation.


Metrics, Cyclomatic complexity, OO metrics, Fault prediction


A. Bacchelli, M. D’Ambros, and M. Lanza. Are popular classes more defect prone? In Proceedings of the 13th International Conference on Fundamental Approaches to Software Engineering, FASE’10, pages 59– 73, Berlin, Heidelberg, 2010. Springer-Verlag.

A. Mockus and L. G. Votta. Identifying reasons for software changes using historic databases. In Proceedings of the International Conference on Software Maintenance, 2000.

C. Bird, N. Nagappan, B. Murphy, H. Gall, and P. Devanbu. Don’t touch my code!: Examining the effects of ownership on software quality. In Proceedings of the 19th ACM SIGSOFT Symposium and the 13th European Conference on Foundations of Software Engineering, ESEC/FSE ’11, pages 4–14, New York, NY, USA, 2011. ACM.

C. Lewis, Z. Lin, C. Sadowski, X. Zhu, R. Ou, and E. J. W. Jr. Does bug prediction support human developers? Findings from a google case study. In International Conference on Software Engineering (ICSE), 2013.

E. Engstrom, P. Runeson, and G. Wikstrand. An empirical evaluation of regression testing based on fixcache ¨ recommendations. In Software Testing, Verification and Validation (ICST), 2010 Third International Conference on, pages 75–78, April 2010.

F. Akiyama. An Example of Software System Debugging. In Proceedings of the International Federation of Information Processing Societies Congress, pages 353–359, 1971.

F. Peters and T. Menzies. Privacy and utility for defect prediction: Experiments with morph. In Proceedings of the 34th International Conference on Software Engineering, ICSE ’12, pages 189–199, Piscataway, NJ, USA, 2012. IEEE Press.

F. Rahman and P. Devanbu. Comparing static bug finders and statistical prediction. In Proceedings of the 2014 International Conference on Software Engineering, ICSE ’14, 2014.

F. Zhang, A. Mockus, I. Keivanloo, and Y. Zou. Towards building a universal defect prediction model. In Proceedings of the 11th Working Conference on Mining Software Repositories, MSR 2014, pages 182– 191, New York, NY, USA, 2014. ACM. 34

J. Nam, S. J. Pan, and S. Kim. Transfer defect learning. In Proceedings of the 2013 International Conference on Software Engineering, ICSE ’13, pages 382–391, Piscataway, NJ, USA, 2013. IEEE Press.

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