Defect Prediction Leads to High Quality Product
Naheed Azeem, Shazia Usmani
DOI: 10.4236/jsea.2011.411075   PDF   HTML     5,576 Downloads   10,278 Views   Citations


Defect prediction is relatively a new research area of software quality assurance. A project team always aims to produce a quality product with zero or few defects. Quality of a product is correlated with the number of defects as well as it is limited by time and by money. So, defect prediction is very important in the field of software quality and software reliability. This paper gives you a vivid description about software defect prediction. It describes the key areas of software defect prediction practice, and highlights some key open issues for the future.

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N. Azeem and S. Usmani, "Defect Prediction Leads to High Quality Product," Journal of Software Engineering and Applications, Vol. 4 No. 11, 2011, pp. 639-645. doi: 10.4236/jsea.2011.411075.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] D. Wahyudin, A. Schatten, D. Winkler, A. M. Tjoa and S. Biffl, “Defect Prediction Using Combined Product and Project Metrics: A Case Study from the Open Source “Apache” MyFaces Project Family,” Proceedings of the 2008 34th Euromicro Conference Software Engineering and Advanced Applications, 2008, pp. 207-215.
[2] H. Zhang, “An Investigation of the Relationships between Lines of Code and Defects,” 2009 IEEE International Conference on Software Maintenance, pp. 274-283. doi:10.1109/ICSM.2009.5306304
[3] R. Moser, W. Pedrycz and G. Succi, “A Comparative Analysis of the Efficiency of Change Metrics and Static Code Attributes for Defect Prediction,” In Proceedings of the International Conference on Software Engineering (ICSE’08), Leipzig, pp. 181-190.
[4] T. Zimmermann and N. Nagappan, “Predicting Defects using Network Analysis on Dependency Graphs,” In Proceedings of the International Conference on Software Engineering (ICSE’08), Leipzig, Germany, pp. 531-540.
[5] Z. A. Rana, S. Shamail and M. M. Awais, “Ineffectiveness of Use of Software Science Metrics as Predictors of Defects in Object Oriented Software,” Proceedings of the 2009 WRI World Congress on Software Engineering, Vol. 04, 2009, pp. 3-7. doi:10.1109/WCSE.2009.92
[6] A. Tosun and A. Bener, “Reducing False Alarms in Software Defect Prediction by Decision Threshold Optimization,” Third International Symposiumm on Empirical Software Engineering and Measurement, 2009 IEEE, pp. 477-480. doi:10.1109/ESEM.2009.5316006
[7] B. Caglayan, A. Bener and S. Koch, “Merits of Using Repository Metrics in Defect Prediction for Open Source Projects,” 2009 ICSE Workshop on Emerging Trends in Free/Libre/Open Source Software Research and Development, pp. 31-36.
[8] J. Ratzinger, T. Sigmund and H. C. Gall, “On the Relation of Refactoring and Software Defects,” In Proceedings of the International Workshop on Mining Software Repositories (MSR ’08), Leipzig, Germany, pp. 35-38.
[9] K. Korhonen and O. Salo, “Exploring Quality Metrics to Support Defect Management Process in a Multi-Site Organization—A Case Study,” 19th International Symposium on Software Reliability Engineering(IEEE '08), pp. 213-218.
[10] M. D’Ambros, M. Lanza and R. Robbes, “On the Relationship between Change Coupling and Software Defects,” Proceedings of the 2009 16th Working Conference on Reverse Engineering, IEEE 2009, pp.135-144.
[11] K. Raaschou and A. Rainer, “Exposure Model for Prediction of Number of Customer Reported Defects,” Proceedings of the Second ACM-IEEE International Symposium on Empirical Software Engineering and Measurement, (ACM ’08), pp. 306-308.
[12] R. Bucholz and P. A. Laplante, “A Dynamic Capture-Recapture Model for Software Defect Prediction,” Innovations in Systems and Software Engineering (2009), Springer London, pp. 265-270.
[13] W. Fan, Y. Xiaohu, Z. Xiaochun and C. Lu, “Simulation of the Defect Removal Process with Queuing Theory,” 3rd International Symposium on Empirical Software Engineering and Measurement, 2009, pp. 473-476.
[14] Y. Hong, J. Baik, I. Y. Ko and H. J. Choi, “A Value-Added Predictive Defect Type Distribution Model based on Project Characteristics,” Seventh IEEE/ACIS International Conference on Computer and Information Science, 2008, pp. 469-474.
[15] Y. Kastro and A. B. Bener, “A Defect Prediction Method for Software Versioning,” Software Quality Journal, Springer Netherlands, Vol. 16, 2008, pp. 543-562.
[16] A. Tosun, B. Turhan and A. Bener, “Practical Considerations in Deploying AI for Defect Prediction: A Case Study within the Turkish Telecommunication Industry,” Proceedings of the 5th International Conference on Predictor Models in Software Engineering, 2009, pp. 24-25.
[17] N. K. Nagwani and S. Verma, “Predictive Data Mining Model for Software Bug Estimation Using Average Weighted Similarity,” IEEE 2nd International Advance Computing Conference, 2010, pp. 373-378.
[18] P. Singh and S. Verma, “An Investigation of the Effect of Discretization on Defect Prediction Using Static Measures,” IEEE International Conference on Advances in Computing, Control, and Telecommunication Technologies, 2009, pp. 837-839. doi:10.1109/ACT.2009.212
[19] R. Hewett, “Mining Software Defect Data to Support Software Testing Management,” Applied Intelligence, Springer Netherlands, 2009.
[20] T. Zimmermann, N. Nagappan, H. Gall, E. Giger and B. Murphy, “Cross-Project Defect Prediction, a Large Scale Experiment on Data vs. Domain vs. Process,” European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE), Amsterdam, 2009, pp. 91-100.

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