An Investigation on the Metric Threshold for Fault- Proneness
Автор: Aarti, Geeta Sikka, Renu Dhir
Журнал: International Journal of Education and Management Engineering(IJEME) @ijeme
Статья в выпуске: 3 vol.7, 2017 года.
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The software quality can be enhanced with the awareness and compassionate about the software faults. We acknowledge the impact of threshold of the object-oriented metrics on fault-proneness. The prediction of fault-prone classes in early stage of the life-cycle assures you to allocate the resources effectively. In this paper, we proposed the logistic regression based statistical method and metric threshold to reduce the false alarm for projects that fall outside the risk range. We presented the threshold effects on public datasets collected from the NASA repository and validated the use of threshold on ivy and jedit datasets. The results concluded that proposed methodology achieves the speculative results with projects having similar characteristic.
Quality, semi-supervised, fault- proneness, false-alarm
Короткий адрес: https://sciup.org/15014062
IDR: 15014062
Список литературы An Investigation on the Metric Threshold for Fault- Proneness
- V. Basili, L. Briand L and W. Melo, A validation of object-oriented design metrics as quality indicators. IEEE Transactions on Software Engineering, vol. 22, no. 10: 1996, pp. 751–761
- K. Ferreira, M. Bigonha, S. Bigonha , L. Mendes and H. Almeida , Identifying thresholds for object–oriented software metrics, Journal of Systems and Software, vol. 85, no.2, 2012, pp. 244–257.
- C. Catal, U. Sevim, and B. Diri, B. Clustering and metrics threshold based software fault prediction of unlabeled program modules, In proceeding of Sixth International Conference on Information Technology: New Generations, 2009, pp. 199-204.
- C. Catal, Software fault prediction: A literature review and current trends, Expert Systems with Applications, vol. 38, no. 4, 2011, pp. 4626-4636.
- S. Chidamber and C. Kemerer, A Metrics Suite for Object-Oriented Design, IEEE Transaction of Software Engineering, vol. 20, no.6, 1994, pp. 476-493.
- T. Hall, S. Beecham, D. Bowes, D. Gray and S. Counsell, A systematic literature review on fault prediction performance in software engineering, IEEE Transaction Software Engineering, vol. 38, no.6, 2012, pp. 1276-1304.
- P. K. Rajput, G. Nagpal and Aarti, Feature weighted unsupervised classification algorithm and adaptation for software cost estimation, Intertaional Journal of Computational Intelligence Studies, vol. 3, no. 1, 2014, pp 74-93.
- P. K. Rajput , G. Nagpal and Aarti , CGANN-Clustered Genetic Algorithm with Neural Network for Software Cost Estimation, In proceeding of International Conference on Advances in Engineering and Technology (ICAET'2014), March 2014, pp 268-272
- R. Perez-Castillo, L. Sánchez-González, M. Piattini, F. Garcia , I. Garcia-Rodriguez, Obtaining thresholds for the effectiveness of business process mining, In proceedings of the International Symposium on Empirical Software Engineering and Measurement, 2011, 453–462
- T. Gyimothy , R. Ferenc, I. Siket, Empirical validation of object-oriented metrics on open source software for fault prediction, IEEE Transactions on Software Engineering,vol. 31, no. 10, 2005, pp.897–910.
- J.M. Bieman and B.K. Kang ,Cohesion and Reuse in an Object-Oriented System. Proceedings of the Symposium on Software reusability-ACM 20: 1995, pp. 259-262
- S. Bhattacharya , S. Rungta and N. Kar N, Software fault prediction using fuzzy clustering & genetic algorithm. International Journal of Digital Application & Contemporary Research, vol. 2, no. 5, 2013, pp. 1-7
- P. K. Rajput, G. Nagpal and Aarti, Feature weighted unsupervised classification algorithm and adaptation for software cost estimation. International Journal of Computational Intelligence Studies, vol.3, no. 1, 2014, pp. 74-93.
- P. K. Rajput, G. Nagpal and Aarti, CGANN-Clustered Genetic Algorithm with Neural Network for Software Cost Estimation. In proceeding of International Conference on Advances in Engineering and Technology (ICAET'2014), 2014, pp. 268-272.
- Aarti, G. Sikka, R. Dhir, An investigation on the effect of cross project data for prediction accuracy. International Journal of System Assurance Engineering and Management, vol. 7, no. 1, 2016, pp. 1-26.
- A. H. Patil, N. Goveas and K. Rangarajan, Regression Test Suite Execution Time Analysis using Statistical Techniques. I.J. Education and Management Engineering, vol. 6, no. 3, 2016, pp. 33-41.
