The week's pick
Random Articles
Reseach Article
Cross Company and within Company Fault Prediction using Object Oriented Metrics
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 74 - Number 8 |
| Year of Publication: 2013 |
| Authors: Pradeep Singh, Shrish Verma, O P Vyas |
10.5120/12903-9587
|
Pradeep Singh, Shrish Verma, O P Vyas . Cross Company and within Company Fault Prediction using Object Oriented Metrics. International Journal of Computer Applications. 74, 8 ( July 2013), 5-11. DOI=10.5120/12903-9587
Abstract
This paper investigates fault predictions in the cross-project context focusing on the object oriented metrics for the companied that do not track fault related data or have no historical records available. In this study, empirical analysis is carried out to validate object-oriented Chidamber and Kemerer (CK) design metrics for cross project fault prediction. The machine learning techniques used for evaluation are J48, NB, SVM, RF, K-NN and DT. The results indicate CK metrics can be used as initial guideline for the projects where no previous fault data is available. Overall, the results of cross company is comparable to the within company data learning. Our analysis is in favour of reusability in object oriented technology and it has been empirically shown that object oriented metric data can be used for cross company fault prediction in initial stage when previous fault data of the project is not available.
References
- Basili, V. R. and B. T. Perricone, 1984. Software errors and complexity: An empirical investigation. Commun. ACM, 27: 42-52. http://portal. acm. org/citation. cfm?id=2085.
- Halstead, M. H. , 1977. Elements of Software Science. 1st Edn. , Elsevier North Holland, New York, ISBN: 10: 0444002057 pp: 127.
- McCabe, T. J. , 1976. A complexity measure. IEEE Trans. Software Eng. , 2: 308-320. DOI: 10. 1109/TSE. 1976. 233837.
- Chidamber, S. R. and C. F. Kemerer, 1994. A metrics suite for object-oriented design. IEEE Trans. Software Eng. , 20: 476-493. DOI:10. 1109/32. 295895.
- Basili, V. R. , L. C. Briand and W. L. Melo, 1996. A validation of object-oriented design metrics as quality indicators. IEEE Trans. Software Eng. , 22: 751-761. DOI: 10. 1109/32. 544352
- Menzies, T. , Greenwald, J. , Frank, A. : Data mining static code attributes to learn fault predictors. IEEE Trans. Softw. Eng. 33(1), 2–13 (2007b)
- Lessmann, S. , Baesens, B. , Mues, C. , Pietsch, S. : Benchmarking classification models for software fault prediction: a proposed framework and novel findings. IEEE Trans. Softw. Eng. 34(4), 485–496 (2008)
- D'Ambros, M. , Lanza, M. , Robbes, R. : An extensive comparison of bug prediction approaches. In: Proceedings of the 7th IEEE Working Conference on Mining Software Repositories, pp. 31–41 (2010)
- Zimmermann, T. , Nagappan, N. , Gall, H. : Cross-project fault 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 Symposium on The Foundations of Software Engineering, pp. 91–100 (2009)
- Watanabe, S. , Kaiya, H. , Kaijiri, K. : Adapting a fault prediction model to allow inter language reuse. In: Proceedings of the InternationalWorkshop on Predictive Models in Software Engineering, pp. 19–24 (2008)
- Turhan, B. , Menzies, T. , Bener, A. : On the relative value of cross-company and within_company data for fault prediction. Empir. Softw. Eng. 14(5), 540–578 (2009)
- Ostrand, T. J. , Weyuker, E. J. , Bell, R. M. : Predicting the location and number of faults in large software systems. IEEE Trans. Softw. Eng. 31(4), 340–355 (2005)
- Boetticher, G. , Menzies, T. , Ostrand, T. J. : PROMISE repository of empirical software engineering data. http://promisedata. org/repository (2007). Accessed 12 December 2010
- Tosun, A. , Bener, A. , Kale, R. : AI-based software fault predictors: applications and benefits in a case study. In: Proceedings of the 22th Innovative Applications of Artificial Intelligence Conference, pp. 1748–1755 (2010)
- Nagappan, N. , Ball, T. : Use of relative code churn measures to predict system fault density. In: Proceedings of the 27th International Conference on Software Engineering, pp. 284–292 (2005)
- Catal, C. , Diri, B. : A systematic review of software fault prediction studies. Expert Syst. Appl. 36(4), 7346–7354 (2009)
- Zimmermann, T. , Nagappan, N. , Gall, H. : Cross-project fault 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 Symposium on The Foundations of Software Engineering, pp. 91–100 (2009)
- Quinlan, J. R. : C4. 5: Programs for Machine Learning. Morgan Kaufmann, San Mateo (1993)
- Watanabe, S. , Kaiya, H. , Kaijiri, K. : Adapting a fault prediction model to allow inter language reuse. In: Proceedings of the InternationalWorkshop on Predictive Models in Software Engineering, pp. 19–24 (2008)
- Vapnik, V. , 1995. The Nature of Statistical Learning Theory. Springer, New York.
- D. Aha, D. Kibler (1991). Instance-based learning algorithms. Machine Learning. 6:37-66.
- Breiman, L. , 2001. Random forests. Machine Learning 45, 5–32.
- C C. Wohlin, P. Runeson, M. Host, M. C. Ohlsson, B. Regnell, and A. Wesslen, Experimentation in Software Engineering: An Introduction. Kluwer Academic Publishers, 2000.
Index Terms
Keywords