Software fault prediction models for Web applications

Proceedings - International Computer Software and Applications Conference

Volumn , Issue , 2010, Pages 51-56

  Giang, Le Truong ^a   Kang, Dongwon ^a   Bae, Doo Hwan ^a  

^a Korea Advanced Institute of Science and Technology (KAIST)   (South Korea)

Author keywords

Fault prediction; Software metrics; Web applica tions

Indexed keywords

DAILY LIVES; ENGINEERING COMMUNITY; EXPERIMENTAL STUDIES; FAULT PREDICTION; PROJECT RISK; SOFTWARE FAULT PREDICTION; SOFTWARE METRICS; SOFTWARE MODULES; SOFTWARE QUALITY; WEB APPLICA TIONS; WEB APPLICATION;

COMPUTER APPLICATIONS; COMPUTER SOFTWARE SELECTION AND EVALUATION; FORECASTING; MATHEMATICAL MODELS; QUALITY ASSURANCE; SOFTWARE ENGINEERING; SOFTWARE TESTING; TECHNICAL PRESENTATIONS;

WORLD WIDE WEB;

EID: 78649881391     PISSN: 07303157     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/COMPSACW.2010.19     Document Type: Conference Paper

References (27)

1. Y. Wu and J. Offutt, "Modeling and testing web-based ap plications," George Mason University, Virginia, Tech. Rep. ISE-TR-02-08, Nov. 2002.
2. L. S. Al-Salem and A. A. Samaha, "Eliciting web application requirements - an industrial case study," The Journal of Systems and Software, vol. 80, no. 3, pp. 294-313, 2007.
3. D. M. Brandon, Ed., Software Engineering for Modern Web Applications: Methodologies and Technologies. NewYork: Information Science Reference, 2008.
4. S. Casteleyn, F. Daniel, P. Dolog, and M. Matera, Engineering Web Applications. Heidelberg: Springer, 2009.
5. C. Catal and B. Diri, "A systematic review of software fault prediction studies," Expert Systems with Applications, vol. 36, no. 4, pp. 7346-7354, 2009.
6. R. Moser, W. Pedrycz, and G. Succi, "A comparative analysis of the efficiency of change metrics and static code attributes for defect prediction," in Proc. ACM/IEEE 30th International Conference on Software Engineering (ICSE'09), Leipzig, Germany, May 2008, pp. 181-190.
7. T. Menzies, J. Greenwald, and A. Frank, "Data mining static code attributes to learn defect predictors," IEEE Transactions on Software Engineering, vol. 33, no. 1, pp. 2-13, Jan. 2007.
8. K. O. Elish and M. O. Elish, "Predicting defect-prone soft ware modules using support vector machines," The Journal of Systems and Software, vol. 81, no. 5, pp. 649-660, 2008.
9. T. Gyimóthy, R. Ferenc, and 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, Oct. 2005.
10. T. L. Graves, A. F. Karr, J. Marron, and H. Siy, "Predicting fault incidence using software change history," IEEE Trans actions on Software Engineering, vol. 26, no. 7, pp. 653-661, Jul. 2000.
11. N. Nagappan, T. Ball, and B. Murphy, "Using historical in-process and product metrics for early estimation of software failures," in Proc. IEEE 17th International Symposium on Software Reliability Engineering, Raleigh, North Carolina, USA, Nov. 2006, pp. 62-74.
12. E. Arisholm, L. C. Briand, and E. B. Johannessen, "A systematic and comprehensive investigation of methods to build and evaluate fault prediction models," The Journal of Systems and Software, vol. 81, no. 11, pp. 2-17, 2010.
13. F. Ricca and P. Tonella, "Detecting anomaly and failure in web applications," IEEE Multimedia, vol. 13, no. 2, pp. 44-51, 2006.
14. Z. Li, N. Alaeddine, and J. Tian, "Multi-faceted quality and defect measurement for web software and source contents," The Journal of Systems and Software, vol. 83, no. 1, pp. 18-28, 2010.
15. M. V. Zelkowitz, Ed., Web Technology, ser. Advances in Computer. Amsterdam: Elsevier Academic Press, 2006, vol. 67.
16. N. Curtis, Modular Web Design: Creating Reusable Com ponents for User Experience Design and Documentation. Berkeley, CA: Peachpi Press, 2010.
17. W3C Standards for Web design and applications: http://www.w3.org/ standards/webdesign/
18. Y. Zhou, B. Xu, and H. Leung, "On the ability of complexity metrics to predict fault-prone classes in object-oriented sys tems," The Journal of Systems and Software, vol. 83, no. 4, pp. 660-674, 2010.
19. Y. Zhou and H. Leung, "Empirical analysis of object-oriented design metrics for predicting high and low severity faults," IEEE Transactions on Software Engineering, vol. 32, no. 10, pp. 771-789, Oct. 2006.
20. Drupal project: http://www.drupal.org/
21. I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, 2nd ed. San Francisco, CA: Morgan Kaufmann, 2005.
22. D. T. Larose, Data Mining Methods and Models. New Jersey: John Wiley and Sons, 2006.
23. D. L. Olson and D. Delen, Advanced Data Mining Techniques. Heidelberg: Springer, 2008.
24. L.-W. Chen and S.-J. Huang, "Accuracy and efficiency com parisons of single- and multi-cycled software classification models," The Journal of Systems and Software, vol. 51, no. 1, pp. 173-181, 2009.
25. J. Han and M. Kamber, Data Mining: Concepts and Tech niques, 2nd ed. San Francisco, CA: Morgan Kaufmann, 2006.
26. M. A. Hall, "Correlation-based feature selection for discrete and numeric class machine learning," in Proc. 17th Interna tional Conference on Machine Learning, CA, USA, Jun./Jul. 2000, pp. 359-366.
27. C. Wohlin, P. Runeson, M. Höst, M. C. Ohlsson, B. Regnell, and A. Wesslén, Experimentation in Software Engineering: An Introduction. Kluwer Academic Press, 2000.