An Intuitionistic Fuzzy K-Medoids Based Similar Pattern Analysis in Software Defect Prediction
||International Journal of Computer Trends and Technology (IJCTT)||
|© 2018 by IJCTT Journal|
|Year of Publication : 2018|
|Authors : M. Jaikumar, V. Kathiresan|
|DOI : 10.14445/22312803/IJCTT-V55P108|
M. Jaikumar, V. Kathiresan "An Intuitionistic Fuzzy K-Medoids Based Similar Pattern Analysis in Software Defect Prediction". International Journal of Computer Trends and Technology (IJCTT) V55(1):41-49, January 2018. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
In the software field the quality and the reliability are the important factors which have to be greatly handled with the help of software defect prediction. During the period of development and the maintenance of the software detecting and rectifying the software defects is really more expensive. By designing prediction model which accurately determines the occurrence of defect in software greatly assist in efficient software testing, reducing the cost and considerably improvising testing process of software by focusing on fault prone modules. Machine Learning Clustering has emerged as a way to predict the fault in the software system by grouping the similar patterns. This paper focuses on predicting the software defect contributed by NASA repository dataset. This work uses Intuitionistic fuzzy K-medoids based clustering for finding the similar pattern among the software defect dataset and design the rule based on it.
1. G. Graham, E.V. Veenendaal, I. Evans, R. Black, “Foundations of Software Testing: ISTQB Certification”, Thomson Learning, United Kingdom, 2007.
2. N.E. Fenton, M. Neil, “A Critique of Software Defect Prediction Models”, IEEE Transactions on Software Engineering, vol. 25, no.5, pp.675-689, 1999.
3. B. Clark, D. Zubrow, “How Good is the Software: A Review of Defect Prediction Techniques”, Carnegie Mellon University, USA, 2001.
4. V. Nayak, D. Naidya, “Defect Estimation Strategies”, Patni Computer Systems Limited, Mumbai, 2003.
5. M. Thangarajan, B. Biswas, “Software Reliability Prediction Model”, Tata Elxsi Whitepaper, 2002.
6. D. Wahyudin, A. Schatten, D. Winkler, A.M. Tjoa, S. Biffl, “Defect Prediction using Combined Product and Project Metrics: A Case Study from the Open Source “Apache” MyFaces Project Family” In Proceedings of Software Engineering and Advanced Applications (SEAA `08), 34th Euromicro Conference, pp. 207-215, 2008.
7. Sinovcic, L. Hribar, “How to Improve Software Development Process using Mathematical Models for Quality Prediction and Element of Six Sigma Methodology”, In Proceedings of the 33rd International Conventionions 2010 (MIPRO 2010), pp. 388-395, 2010.
8. E.J. Weyuker, T.J. Ostrand, R.M. Bell, “Using Developer Information as a Factor for Fault Prediction”, In Proceedings of the Third International Workshop on Predictor Models in Software Engineering (PROMISE`07), pp.8, 2007.
9. 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, pp. 897-910, 2005.
10. J.S. Collofello, “Simulating the System Test Phase of the Software Development Life Cycle”, In Proceedings of the 2002 Summer Software Computer Simulation Conference, 2002.
11. L. RadliRski, “Predicting Defect Type in Software Projects”, Polish Journal of Environmental Studies, vol.18, no. 3B, pp. 311-315, 2009.
12. M. Staron, W. Meding, “Defect Inflow Prediction in Large Software Projects”, e-Informatica Software Engineering Journal, vol. 4, no. 1, pp. 1-23, 2010.
13. T. Fehlmann, “Defect Density Prediction with Six Sigma”, Presentation in Software Measurement European Forum, 2009.
14. S.W. Haider, J.W. Cangussu, K.M.L. Cooper, R. Dantu, “Estimation of Defects Based on Defect Decay Model: ED3M”, IEEE Transactions on Software Engineering, vol. 34, no. 3, pp. 336-356, 2008.
15. L. Zawadski, T. Orlova, “Building and Using a Defect Prediction Model”, Presentation in Chicago Software Process Improvement Network, 2012.
16. M. Li, H. Zhang, R. Wu, Z.H. Zhou, “Sample-based Software Defect Prediction with Active and Semi-supervised Learning”, Journal of Automated Software Engineering, vol. 19, no. 2, pp. 201-230, 2012.
17. Z. He, F. Shu, Y. Yang, M. Li, Q. Wang, “An Investigation on the Feasibility of Cross-Project Defect Prediction”, Journal of Automated Software Engineering, vol. 19, no. 2, pp. 167-199, 2012.
18. T.J. Ostrand, E.J. Weyuker, “How to Measure Success of Fault Prediction Models”, In Proceedings of Fourth International Workshop on Software Quality Assurance 2007 (SOQUA ’07), pp. 25-30, 2007.
19. L.P. Li, M. Shaw, J. Herbsleb, “Selecting a Defect Prediction Model for Maintenance Resource Planning and Software Insurance”, In Proceedings of 5th Workshop on Economics-Driven Software Engineering Research (EDSER `03), pp. 32-37, 2003.
20. M. D’Ambros, M. Lanza, R. Robbes, “Evaluating Defect Prediction Approaches: A Benchmark and an Extensive Comparison, Journal of Empirical Software Engineering, vol. 17, no. 4-5, pp. 531-577, 2012.
21. Kaufman, L. and Rousseeuw, P.J. (1987), Clustering by means of Medoids, in Statistical Data Analysis Based on the Norm and Related Methods, edited, North-Holland, 405–416.
Software, defect, intuitionistic fuzzy, K-medoids, clustering, fault