A Conceptual Framework for Software Fault Prediction Using Neural Networks
Software testing is a very expensive and critical activity in the software systems’ life-cycle. Finding software faults or bugs is also time-consuming, requiring good planning and a lot of resources. Therefore, predicting software faults is an important step in the testing process to significantly increase efficiency of time, effort and cost usage.
In this study we investigate the problem of Software Faults Prediction (SFP) based on Neural Network. The main contribution is to empirically establish the combination of Chidamber and Kemer software metrics that offer the best accuracy for faults prediction with numeric estimations by using feature selection. We also proposed a conceptual framework that integrates the model for fault prediction.
KeywordsSoftware faults Software metrics Machine learning
- 1.e Abreu, F.B., Melo, W.L.: Evaluating the impact of object-oriented design on software quality. In: 3rd IEEE International Software Metrics Symposium (METRICS 1996), From Measurement to Empirical Results, March 25–26, 1996, Berlin, Germany, pp. 90–99 (1996)Google Scholar
- 5.D’Ambros, M., Lanza, M., Robbes, R.: An extensive comparison of bug prediction approaches. In: Proceedings of MSR 2010 (7th IEEE Working Conference on Mining Software Repositories), pp. 31–41. IEEE CS Press (2010)Google Scholar
- 7.Gao, J.: Machine learning applications for data center optimization. Technical report, Google (2014)Google Scholar
- 10.Isong, B., Ekabua, O.O.: State-of-the-art in empirical validation of software metrics for fault proneness prediction: Systematic review. CoRR abs/1601.01447 (2016)Google Scholar
- 11.Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
- 17.Marinescu, R.: Measurement and Quality in Object Oriented Design. Ph.D. thesis, Faculty of Automatics and Computer Science, University of Timisoara (2002)Google Scholar
- 18.Nair, V., Hinton, G.E.: Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (ICML 2010), pp. 807–814 (2010)Google Scholar
- 20.Sayyad Shirabad, J., Menzies, T.: The PROMISE Repository of Software Engineering Databases. School of Information Technology and Engineering, University of Ottawa, Canada (2005). http://promise.site.uottawa.ca/SERepository
- 21.Serban, C.: Metrics in Software Assessment. Ph.D. thesis, Faculty of Mathematics and Computer Science, Babes-Bolyai University (2012)Google Scholar
- 22.Tanh, M., Kao, M., Chen, M.: An empirical study on object-oriented metrics. In: 6th IEEE International Software Metrics Symposium (METRICS 1999), 4–6 November 1999, Boca Raton, FL, USA. pp. 242–249 (1999)Google Scholar
- 24.Zimmermann, T., Nagappan, N., Gall, H.C., Giger, E., Murphy, B.: Cross-project defect prediction: a large scale experiment on data vs. domain vs. process. In: Proceedings of the 7th joint meeting of the European Software Engineering Conference and the ACM SIGSOFT International Symposium on Foundations of Software Engineering, 2009, Amsterdam, The Netherlands, August 24–28, 2009, pp. 91–100 (2009)Google Scholar