Training on errors experiment to detect fault-prone software modules by spam filter

6th Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering, ESEC/FSE 2007

Volumn , Issue , 2007, Pages 405-414

  Mizuno, Osamu ^a   Kikuno, Tohru ^a  

^a OSAKA UNIVERSITY   (Japan)

Author keywords

Fault prone modules; Spam filter; Text mining

Indexed keywords

CODES (SYMBOLS); DATA MINING; ERROR ANALYSIS; FAULT TOLERANCE; QUALITY CONTROL; TEXT PROCESSING; COMPUTER SOFTWARE; COMPUTER SOFTWARE SELECTION AND EVALUATION; ELECTRONIC MAIL; ERRORS; EXPERIMENTS; INTERNET; SOFTWARE ENGINEERING; SPAMMING; WAVE FILTERS;

FAULT-PRONE MODULES; SPAM FILTERS; TEXT MINING; DETECTION APPROACHES; EARLY STAGES; LOW COSTS; OPEN SOURCE PROJECTS; SOFTWARE METRICS; SOFTWARE MODULES; SOFTWARE QUALITIES; SOURCE CODE REPOSITORIES; SOURCE CODES; SPAM E-MAILS; SPAM FILTER; SPAM FILTERING TECHNIQUES; TEXT FILES; TEXT MININGS;

COMPUTER AIDED SOFTWARE ENGINEERING; FAULT DETECTION;

EID: 37849003535     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1287624.1287683     Document Type: Conference Paper

References (26)

1. P. Bellini, I. Bruno, P. Nesi, and D. Rogai. Comparing fault-proneness estimation models. In Proc. of 10th IEEE International Conference on Engineering of Complex Computer Systems (ICECCS'05), pages 205-214, 2005.
2. bogofilter. http://bogofllter.sourceforge.net/.
3. L. C. Briand, W. L. Melo, and J. Wust. Assessing the applicability of fault-proneness models across object-oriented software projects. IEEE Trans. on Software Engineering, 28(7):706-720, 2002.
4. S. Chhabra, W. S. Yerazunis, and C. Siefkes. Spam filtering using a markov random field model with variable weighting schémas. In Proc. of Fourth IEEE International Conference on Data Mining (ICDM 2004), pages 347-350, 2004.
5. CRM114 - the Controllable Regex Mutilator. http://crm114. sourceforge.net/.
6. G. Denaro and M. Pezze. An empirical evaluation of fault-proneness models. In Proc. of 24th International Conference on Software Engineering (ICSE '02), pages 241-251, 2002.
7. Eclipse Project, http://www.eclipse.org/.
8. P. Graham. Hackers and Painters: Big Ideas from the Computer Age, chapter 8, pages 121-129. O'Reilly Media, 2004.
9. L. Guo, B. Cukic, and H. Singh. Predicting fault prone modules by the dempster-shafer belief networks. In Proc. of 18th IEEE International Conference on Automated Software Engineering (ASE'03), pages 249-252, 2003.
10. T. Kamiya, S. Kusumoto, and K. Inoue. Ccfinder: A multi-linguistic token-based code clone detection system for large scale source code. IEEE Trans. on Software Engineering, 28(7): 654-670, 2002.
11. T. M. Khoshgoftaar and E. B. Allen. Logistic regression modeling of software quality. International Journal of Reliability, Quality and Safety Engineeering, 6(4):303-317, 1999.
12. T. M. Khoshgoftaar and E. B. Allen. Controlling overfitting in classification tree models of software quality. Empirical Software Engineering, 6(1):59-79, 2001.
13. T. M. Khoshgoftaar, E. B. Allen, and J. Deng. Using regressin trees to classify fault-prone software modules. IEEE Transactions on Reliability, 51(4):455-462, 2002.
14. T. M. Khoshgoftaar and N. Seliya. Software quality classification modeling using SPRINT decision tree algorithm. In Proc. of 14th International Conference on Tools with Artificial Intelligence, pages 365-374, 2002.
15. T. M. Khoshgoftaar and N. Seliya. Comparative assessment of software quality classification techniques: An empirical study. Empirical Software Engineering, 9:229-257, 2004.
16. T. M. Khoshgoftaar, R. Shan, and E. B. Allen. Using product, process, and execution metrics to predict fault-prone software modules with classification trees. In Fifth IEEE International Symposium on High Assurance Systems Engineering (HASE'00), pages 301-310, 2000.
17. T. Menzies, J. Greenwald, and A. Frank. Data mining static code attributes to learn defect predictors. IEEE Trans. on Software Engineering, 33(1):2-13, January 2007.
18. O. Mizuno, S. Ikami, S. Nakaichi, and T. Kikuno. Spam filter based approach for finding fault-prone software modules. In Proc. of 2007 International Workshop on Mining Software Repositories (MSR2007), page 4, 2007.
19. NASA's Metrics Data Program, http://mdp.ivv.nasa.gov/.
20. POPFile. http://popfile.sourceforge.net/.
21. Postini Inc. Postini Announces Top Five 2007 Messaging Security Predictions As Email Spam Becomes Front Burner Issue Again In The New Year. http://www.postini.com/news.events/pr/pr120606.php.
22. N. Seliya, T. M. Khoshgoftaar, and S. Zhong. Analyzing software quality with limited fault-proneness defect data. In Proc. of Ninth IEEE International Symposium on High-Assurance Systems Engineering (HASE'05), pages 89-98, 2005.
23. C. Siefkes, F. Assis, S. Chhabra, and W. S. Yerazunis. Combining winnow and orthogonal sparse bigrams for incremental spam filtering. In Proc. of Conference on Machine Learning (ECML) I European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD), 2004.
24. J. Sliwerski, T. Zimmermann, and A. Zeller. When do changes induce fixes? (on fridays.). In Proc. of Mining Software Repository 2005, pages 24-28, 2005.
25. SpamAssassin. http://spamassassin.apache.org/index.html.
26. C. Wohlin, P. Runeson, M. Host, M. C. Ohlsson, B. Regnell, and A. Wesslén. Experimentation in software engineering: An introduction. Kluwer Academic Publishers, 2000.