Software defect prediction using relational association rule mining

Information Sciences

Volumn 264, Issue , 2014, Pages 260-278

  Czibula, Gabriela ^a   Marian, Zsuzsanna ^a   Czibula, Istvan Gergely ^a  

^a BABE BOLYAI UNIVERSITY   (Romania)

Author keywords

Association rule; Data mining; Defect prediction; Software engineering

Indexed keywords

EID: 84894432505     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2013.12.031     Document Type: Article

References (52)

1. R. Agrawal, and R. Srikant Fast algorithms for mining association rules in large databases Proceedings of the 20th International Conference on Very Large Data Bases 1994 Morgan Kaufman Publishers Inc. San Francisco, CA, USA 487 499
2. M. Baojun, K. Dejaeger, J. Vanthienen, B. Baesens, Software defect prediction based on association rule classification, Open Access publications from Katholieke Universiteit Leuven urn:hdl:123456789/296322, Katholieke Universiteit Leuven (February 2011).
3. E. Baralis, L. Cagliero, T. Cerquitelli, and P. Garza Generalized association rule mining with constraints Inform. Sci. 194 2012 68 84
4. G.D. Boetticher, Advances in Machine Learning Applications in Software Engineering, IGI Global, 2007 (Ch. Improving the Credibility of Machine Learner Models in Software Engineering).
5. L.C. Briand, W.L. Melo, and J. Wust Assessing the applicability of fault-proneness models across object-oriented software projects IEEE Trans. Softw. Eng. 28 7 2002 706 720
6. A. Campan, G. Serban, T.M. Truta, and A. Marcus An algorithm for the discovery of arbitrary length ordinal association rules DMIN 2006 107 113
7. V.U.B. Challagulla, F.B. Bastani, I.-L. Yen, and R.A. Paul Empirical assessment of machine learning based software defect prediction techniques Proceedings of the 10th IEEE International Workshop on Object-Oriented Real-Time Dependable Systems, WORDS '05 2005 IEEE Computer Society Washington, DC, USA 263 270
8. R. hua Chang, X. Mu, and L. Zhang Software defect prediction using non-negative matrix factorization J. Softw. 6 11 2011 2114 2120
9. S.R. Chidamber, C.F. Kemerer, Towards a metrics suite for object-oriented design, in: Conference of the Proceedings on Object Oriented Programming Systems, Languages, and Applications, 1991, pp. 197-211.
10. M. D'Ambros, M. Lanza, and R. Robbes Evaluating defect prediction approaches: a benchmark and an extensive comparison Int. J. Emp. Softw. Eng. 2011 1 47
11. T. Fawcett An introduction to roc analysis Pattern Recogn. Lett. 27 8 2006 861 874
12. R.B. Grady Practical Software Metrics for Project Management and Process Improvement 1992 Prentice-Hall Inc. Upper Saddle River, NJ, USA
13. D. Gray, D. Bowes, N. Davey, Y. Sun, B. Christianson, Further thoughts on precision, in: 15th Annual Conference on Evaluation & Assessment in Software Engineering, 2011, pp. 129-133.
14. D. Gray, D. Bowes, N. Davey, Y. Sun, B. Christianson, The misuse of the NASA metrics data program data sets for automated software defect prediction, in: Proceedings of the Evaluation and Assesment in Software Engineering, 2011, pp. 96-103.
15. L. Guo, Y. Ma, B. Cukic, and H. Singh Robust prediction of fault-proneness by random forests ISSRE 2004 417 428
16. A.S. Haghighi, M.A. Dezfuli, and S. Fakhrahmad Applying mining schemes to software fault prediction: a proposed approach aimed at test cost reduction Proceedings of the World Congress on Engineering 2012 WCE 2012 vol. I 2012 IEEE Computer Society Washington, DC, USA 1 5
17. T. Hall, S. Beecham, D. Bowes, D. Gray, and S. Counsell A systematic literature review on fault prediction performance in software engineering IEEE Trans. Softw. Eng. 38 6 2012 1276 1304
18. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I.H. Witten The weka data mining software: an update SIGKDD Explor. 11 1 2009 10 18
19. J. Han Data Mining: Concepts and Techniques 2005 Morgan Kaufman Publishers Inc. San Francisco, CA, USA
20. S. Henry, and D. Kafura Software structure metrics based on information flow IEEE Trans. Softw. Eng. 7 5 1981 510 518
21. R.C. Holte Very simple classification rules perform well on most commonly used datasets Mach. Lear. 1993 63 91
22. Y. Jiang, B. Cukic, and Y. Ma Techniques for evaluating fault prediction models Emp. Softw. Eng. 13 5 2008 561 595
23. Y. Jiang, M. Li, and Z.-H. Zhou Software defect detection with rocus J. Comp. Sci. Technol. 26 2 2011 328 342
24. Y. Kamei, A. Monden, S. Morisaki, K. ichi Matsumoto, A hybrid faulty module prediction using association rule mining and logistic regression analysis, in: Proceedings of the International Symposium on Empirical Software Engineering and Measurements (ESEM), 2008, pp. 279-281.
25. K. Kaminsky, G.D. Boetticher, How to predict more with less defect prediction using machine learners in an implicitly data starved domain < http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.83.4402 >.
26. M.P.J. Kaur, and M. Pallavi Data mining techniques for software defect prediction Int. J. Softw. Web Sci. 1 3 2013 54 57
27. N. Lavrač, B. Kavšek, P. Flach, and L. Todorovski Subgroup discovery with cn2-sd J. Mach. Learn. Res. 5 2004 153 188
28. M. Li, H. Zhang, R. Wu, and Z.-H. Zhou Sample-based software defect prediction with active and semi-supervised learning Auto. Softw. Eng. 19 2 2012 201 230
29. B. Liu, W. Hsu, Y. Ma, Integrating classification and association rule mining, in: Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining (KDD), 1998, pp. 80-86.
30. B. Liu, Y. Ma, C.-K. Wong, Data Mining for Scientific and Engineering Applications, Kluwer Academic, 2001 (Ch. Classification Using Association Rules: Weaknesses and Enhancements).
31. V. López, A. Fernández, S. García, V. Palade, and F. Herrera An insight into classification with imbalanced data: empirical results and current trends on using data intrinsic characteristics Inform. Sci. 250 2013 113 141
32. S.G. Maisikeli, Aspect mining using self-organizing maps with method level dynamic software metrics as input vectors, Ph.D. thesis, Graduate School of Computer and Information Sciences Nova Southeastern University, 2009.
33. A. Marcus, J.I. Maletic, and K.-I. Lin Ordinal association rules for error identification in data sets Proceedings of the Tenth International Conference on Information and Knowledge Management, CIKM '01 2001 ACM New York, NY, USA 589 591
34. Z. Marian, G. Czibula, and I.G. Czibula Using software metrics for automatic software design improvement Stud. Inform. Control 21 3 2012 249 258
35. T. Menzies, J. Greenwald, and A. Frank Data mining static code attributes to learn defect predictors IEEE Trans. Softw. Eng. 33 1 2007 2 13
36. T. Menzies, B. Caglayan, Z. He, E. Kocaguneli, J. Krall, F. Peters, B. Turhan, The promise repository of empirical software engineering data, June 2012 < http://promisedata.googlecode.com >.
37. T. Menzies, and M. Shepperd Special issue on repeatable results in software engineering prediction Emp. Softw. Eng. 17 2012 1 17
38. B. Minaei-Bidgoli, R. Barmaki, and M. Nasiri Mining numerical association rules via multi-objective genetic algorithms Inform. Sci. 233 2013 15 24
39. T.M. Mitchell Machine Learning 1997 McGraw-Hill New York
40. NASA independent verification& validation facility < http://www.nasa.gov/centers/ivv/home/index.html >.
41. NASA software defect datasets < http://nasa-softwaredefectdatasets. wikispaces.com/ >.
42. Bin Pe, Suyun Zhao, Hong Chen, Xuan Zhou, and Dingjie Chen FARP: Mining fuzzy association rules from a probabilistic quantitative database Inform. Sci. 237 2013 242 260
43. N.J. Pizzi A fuzzy classifier approach to estimating software quality Inform. Sci. 241 2013 1 11
44. M.S. Rawat, and S.K. Dubey Software defect prediction models for quality improvement: a literature study Int. J. Comp. Sci. Iss. 9 2 2012 288 296
45. D. Rodríguez, R. Ruiz, J.C. Riquelme, and J.S.N. Aguilar-Ruiz Searching for rules to detect defective modules: a subgroup discovery approach Inform. Sci. 191 2012 14 30
46. M. Shepperd, Q. Song, Z. Sun, and C. Mair Data quality: some comments on the NASA software defect data sets IEEE Trans. Softw. Eng. 99 2013 1 (PrePrints)
47. F. Simon, F. Steinbruckner, and C. Lewerentz Metrics based refactoring CSMR '01: Proceedings of the Fifth European Conference on Software Maintenance and Reengineering 2001 IEEE Computer Society Washington, DC, USA 30 38
48. G. Serban, A. Câmpan, and I.G. Czibula A programming interface for finding relational association rules Int. J. Comput., Commun. Control I (S.) 2006 439 444
49. Q. Song, Z. Jia, M. Shepperd, S. Ying, and J. Liu A general software defect proneness prediction framework IEEE Trans. Softw. Eng. 37 3 2011 356 370
50. C. Spearman The proof and measurement of association between two things Am. J. Psychol. 15 1904 72 101
51. S. Stehman Selecting and interpreting measures of thematic classification accuracy Rem. Sens. Environ. 62 1 1997 77 89
52. P.-N. Tan, M. Steinbach, and V. Kumar Introduction to Data Mining first ed. 2005 Addison-Wesley, Longman Publishing Co., Inc. Boston, MA, USA