A study of subgroup discovery approaches for defect prediction

Information and Software Technology

Volumn 55, Issue 10, 2013, Pages 1810-1822

  Rodriguez, Daniel ^a   Ruiz, Roberto ^b   Riquelme, Jose C ^c   Harrison, Rachel ^d  

^a UNIVERSITY OF ALCALÁ   (Spain)

^b UNIVERSIDAD PABLO DE OLAVIDE   (Spain)

^c UNIVERSITY OF SEVILLE   (Spain)

^d OXFORD BROOKES UNIVERSITY   (United Kingdom)

Author keywords

Defect prediction; Imbalanced datasets; Rules; Subgroup discovery

Indexed keywords

CLASSIFICATION TECHNIQUE; DEFECT PREDICTION; IMBALANCED DATA-SETS; MACHINE LEARNING APPROACHES; PREPROCESSING TECHNIQUES; RULES; SOFTWARE DEFECT PREDICTION; SUBGROUP DISCOVERY;

ALGORITHMS; CLASSIFICATION (OF INFORMATION); FORECASTING;

DEFECTS;

EID: 84880777880     PISSN: 09505849     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.infsof.2013.05.002     Document Type: Article

References (75)

1. R. Agrawal, T. Imieliński, and A. Swami Mining association rules between sets of items in large databases SIGMOD Record 22 1993 207 216
2. E. Arisholm, and L.C. Briand Predicting fault-prone components in a Java legacy system Proceedings of the 2006 ACM/IEEE International Symposium on Empirical Software Engineering (IESE'06) 2006 ACM New York, NY, USA 8 17 (Pubitemid 46634436)
3. E. Arisholm, L.C. Briand, and E.B. Johannessen A systematic and comprehensive investigation of methods to build and evaluate fault prediction models Journal of Systems and Software 83 2010 2 17
4. D. Azar, H. Harmanani, and R. Korkmaz A hybrid heuristic approach to optimize rule-based software quality estimation models Information and Software Technology 51 2009 1365 1376
5. D. Azar, and J. Vybihal An ant colony optimization algorithm to improve software quality prediction models: case of class stability Information and Software Technology 53 2011 388 393
6. S.D. Bay, and M.J. Pazzani Detecting group differences: mining contrast sets Data Mining and Knowledge Discovery 5 2001 213 246
7. S. Benlarbi, K.E. Emam, N. Goel, S.N. Rai, Thresholds for object-oriented measures, in: 11th International Symposium on Software Reliability Engineering (ISSRE 2000), pp. 24-39.
8. S. Bibi, G. Tsoumakas, I. Stamelos, I. Vlahvas, Software defect prediction using regression via classification, in: IEEE International Conference on Computer Systems and Applications (AICCSA 2006), pp. 330-336.
9. L. Breiman Random forests Machine Learning 45 2001 5 32 (Pubitemid 32933532)
10. L. Briand, P. Devanbu, and W. Melo An investigation into coupling measures for C++ Proceedings of the 19th International Conference on Software Engineering (ICSE'97), ICSE'97 1997 ACM New York, NY, USA 412 421
11. K. Brune, D. Fisher, J. Foreman, J. Gross, R. Rosenstein, M. Bray, W. Mills, D. Sadoski, J. Shimp, E. van Doren, C. Vondrak, M. Gerken, G. Haines, E. Kean, M.D. Luginbuhl, C4 Software Technology Reference Guide: A Prototype, Technical Report CMU/SEI-97-HB-001, Software Engineering Institute (SEI), Carnegie Mellon University, 1997.
12. C. Catal, and B. Diri A systematic review of software fault prediction studies Expert Systems with Applications 36 2009 7346 7354
13. S. Chidamber, and C. Kemerer A metrics suite for object oriented design IEEE Transactions on Software Engineering 20 1994 476 493
14. P. Clark, and T. Niblett The CN2 induction algorithm Machine Learning 3 1989 261 283
15. J. Clarke, J. Dolado, M. Harman, R. Hierons, B. Jones, M. Lumkin, B. Mitchell, S. Mancoridis, K. Rees, M. Roper, and M. Shepperd Reformulating software engineering as a search problem IEE Software 150 2003 161 175
16. M. D'Ambros, M. Lanza, R. Robbes, An extensive comparison of bug prediction approaches, in: 7th IEEE Working Conference on Mining Software Repositories (MSR 2010), pp. 31-41.
17. M. D'Ambros, M. Lanza, and R. Robbes Evaluating defect prediction approaches: a benchmark and an extensive comparison Empirical Software Engineering 17 2012 531 577
18. G. Denaro, M. Pezze, An empirical evaluation of fault-proneness models, in: Proceedings of the 24rd International Conference on Software Engineering (ICSE 2002), 2002, pp. 241-251. (Pubitemid 35009114)
19. G. Dong, and J. Li Efficient mining of emerging patterns: discovering trends and differences Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '99 1999 ACM New York, NY, USA 43 52
20. K.O. Elish, and M.O. Elish Predicting defect-prone software modules using support vector machines Journal of Systems and Software 81 2008 649 660 (Pubitemid 351389562)
21. T. Fawcett An introduction to ROC analysis Pattern Recognition Letters 27 2006 861 874
22. U.M. Fayyad, and K.B. Irani On the handling of continuous-valued attributes in decision tree generation Machine Learning 8 1992 87 102
23. U.M. Fayyad, G. Piatetsky-Shapiro, and P. Smyth From data mining to knowledge discovery in databases AI Magazine 17 1996 37 54
24. N. Fenton, and M. Neil A critique of software defect prediction models IEEE Transactions on Software Engineering 25 1999 675 689 (Pubitemid 30541838)
25. D. Gamberger, and N. Lavrac Expert-guided subgroup discovery: methodology and application Journal of Artificial Intelligence Research 17 2002 501 527
26. T. Gyimothy, R. Ferenc, and I. Siket Empirical validation of object-oriented metrics on open source software for fault prediction IEEE Transactions on Software Engineering 31 2005 897 910 (Pubitemid 43065752)
27. M. Halkidi, D. Spinellis, G. Tsatsaronis, and M. Vazirgiannis Data mining in software engineering Intelligent Data Analysis 15 2011 413 441
28. M. Hall, and G. Holmes Benchmarking attribute selection techniques for discrete class data mining IEEE Transactions on Knowledge and Data Engineering 15 2003 1437 1447
29. T. Hall, S. Beecham, D. Bowes, D. Gray, S. Counsell, A systematic literature review on fault prediction performance in software engineering, IEEE Transactions on Software Engineering 38 (2012) 1276-1304.
30. M. Halstead Elements of software science Operating And Programming Systems Series vol. 2 1977 Elsevier Computer Science Library, Elsevier New York, Oxford
31. R. Harrison, S. Counsell, and R. Nithi Experimental assessment of the effect of inheritance on the maintainability of object-oriented systems The Journal of Systems and Software 52 2000 173 179
32. F. Herrera, C.J. Carmona del Jesus, P. González, and M.J. del Jesus An overview on subgroup discovery: foundations and applications Knowledge and Information Systems 29 2011 495 525
33. D.A. Houari A. Sahraoui, Quality estimation models optimization using genetic algorithms: case of maintainability, in: Proc. of the 2nd European Software Measurement Conference (FESMA'99).
34. T.M. Khoshgoftaar, E. Allen, and J. Deng Using regression trees to classify fault-prone software modules IEEE Transactions on Reliability 51 2002 455 462 (Pubitemid 35369801)
35. T.M. Khoshgoftaar, E. Allen, J. Hudepohl, and S. Aud Application of neural networks to software quality modeling of a very large telecommunications system IEEE Transactions on Neural Networks 8 1997 902 909 (Pubitemid 127765148)
36. T.M. Khoshgoftaar, and N. Seliya Analogy-based practical classification rules for software quality estimation Empirical Software Engineering 8 2003 325 350
37. T.M. Khoshgoftaar, and N. Seliya Comparative assessment of software quality classification techniques: an empirical case study Empirical Software Engineering 9 2004 229 257
38. K. Kira, and L.A. Rendell A practical approach to feature selection Proceedings of the Ninth International Workshop on Machine Learning, ML92 1992 Morgan Kaufman Publishers Inc. San Francisco, CA, USA 249 256
39. W. Klösgen,Explora: a multipattern and multistrategy discovery assistant, in: Advances in Knowledge Discovery and Data Mining, 1996, American Association for Artificial Intelligence Menlo Park, CA, USA, pp. 249-271.
40. I. Kononenko Estimating attributes: analysis and extensions of relief Proceedings of the European Conference on Machine Learning (ECML'94), ECML-94 1994 Springer Verlag New York, Inc., Secaucus, NJ, USA 171 182
41. A.G. Koru, and H. Liu Building effective defect-prediction models in practice IEEE Software 22 2005 23 29 (Pubitemid 41709721)
42. P. Kralj, N. Lavrač, B. Zupan, Subgroup visualization, in: Proceedings of the 8th International Multiconference Information Society (IS 2005), pp. 228-231.
43. P. Kralj Novak, N. Lavrč, and G.I. Webb Supervised descriptive rule discovery: a unifying survey of contrast set, emerging pattern and subgroup mining Journal of Machine Learning Research 10 2009 377 403
44. N. Lavrač, B. Kavšek, P. Flach, and L. Todorovski Subgroup discovery with CN2-SD The Journal of Machine Learning Research 5 2004 153 188
45. S. Lessmann, B. Baesens, C. Mues, and S. Pietsch Benchmarking classification models for software defect prediction: a proposed framework and novel findings IEEE Transactions on Software Engineering 34 2008 485 496
46. R. Lincke, J. Lundberg, and W. Löwe Comparing software metrics tools Proceedings of the 2008 International Symposium on Software Testing and Analysis (ISSTA'08), ISSTA'08 2008 ACM New York, NY, USA 131 142
47. T. McCabe A complexity measure IEEE Transactions on Software Engineering 2 1976 308 320
48. T.J. McCabe, and C.W. Butler Design complexity measurement and testing Communications of the ACM 32 1989 1415 1425
49. T. Mende, and R. Koschke Revisiting the evaluation of defect prediction models Proceedings of the 5th International Conference on Predictor Models in Software Engineering (PROMISE'09) 2009 ACM New York, NY, USA 1 10
50. T. Mende, and R. Koschke Effort-aware defect prediction models Proceedings of the 2010 14th European Conference on Software Maintenance and Reengineering (CSMR'10), CSMR'10 2010 IEEE Computer Society Washington, DC, USA 107 116
51. T. Menzies, A. Butcher, D. Cok, A. Marcus, L. Layman, F. Shull, B. Turhan, T. Zimmermann, Local vs. global lessons for defect prediction and effort estimation, IEEE Transactions on Software Engineering (2012a) (preprint).
52. T. Menzies, B. Caglayan, E. Kocaguneli, J. Krall, F. Peters, B. Turhan, The PROMISE Repository of Empirical Software Engineering Data, 2012.
53. T. Menzies, A. Dekhtyar, J. Distefano, and J. Greenwald Problems with precision: a response to comments on data mining static code attributes to learn defect predictors IEEE Transactions on Software Engineering 33 2007 637 640 (Pubitemid 47322970)
54. T. Menzies, J. Greenwald, and A. Frank Data mining static code attributes to learn defect predictors IEEE Transactions on Software Engineering 33 2007 2 13 (Pubitemid 46002165)
55. T. Menzies, Z. Milton, B. Turhan, B. Cukic, Y. Jiang, and A. Bener Defect prediction from static code features: currentresults, limitations, new approaches Automated Software Engineering 17 2010 375 407 10.1007/s10515-010- 0069-5
56. I. Myrtveit, E. Stensrud, and M. Shepperd Reliability and validity in comparative studies of software prediction models IEEE Transactions on Software Engineering 31 2005 380 391 (Pubitemid 41046926)
57. Y. Peng, G. Kou, G. Wang, H. Wang, and F. Ko Empirical evaluation of classifiers for software risk management International Journal of Information Technology & Decision Making (IJITDM) 08 2009 749 767
58. Y. Peng, G. Wang, H. Wang, User preferences based software defect detection algorithms selection using MCDM, Information Sciences, 191 (2012) 3-13.
59. J. Quinlan C4.5: Programs for Machine Learning 1993 Morgan Kaufmann San Mateo, California
60. D. Radjenović, M. Heričo, R. Torkar, A. Živkovič, Software fault prediction metrics: a systematic literature review, Information and Software Technology, 55 (2013) 1397-1418, http://dx.doi.org/10.1016/j.infsof.2013.02.009.
61. D. Rodriguez, R. Ruiz, J. Cuadrado, J. Aguilar-Ruiz, Detecting fault modules applying feature selection to classifiers, in: IEEE International Conference on Information Reuse and Integration (IRI'07), pp. 667-672.
62. D. Rodriguez, R. Ruiz, J. Riquelme, and J. Aguilar-Ruiz Searching for rules to detect defective modules: a subgroup discovery approach Information Sciences 191 2012 14 30
63. R. Shatnawi, W. Li, J. Swain, and T. Newman Finding software metrics threshold values using ROC curves Journal of Software Maintenance and Evolution: Research and Practice 22 2010 1 16
64. M. Shepperd, and G. Kadoda Comparing software prediction techniques using simulation IEEE Transactions on Software Engineering 27 2001 1014 1022 (Pubitemid 33106029)
65. Y. Singh, A. Kaur, and R. Malhotra Empirical validation of object-oriented metrics for predicting fault proneness models Software Quality Journal 18 2010 3 35 10.1007/s11219-009-9079-6
66. Q. Song, M. Shepperd, M. Cartwright, and C. Mair Software defect association mining and defect correction effort prediction IEEE Transactions on Software Engineering 32 2006 69 82 (Pubitemid 43323904)
67. O. Vandecruys, D. Martens, B. Baesens, C. Mues, M. De Backer, and R. Haesen Mining software repositories for comprehensible software fault prediction models Journal of Systems and Software 81 2008 823 839 (Pubitemid 351389560)
68. A.H. Watson, T.J. McCabe, and D.R. Wallace Structured testing: a testing methodology using the cyclomatic complexity metric NIST Special Publication 500 1996 1 114
69. I. Witten, E. Frank, and M. Hall Data Mining: Practical Machine Learning Tools and Techniques third ed. 2011 Morgan Kaufmann San Francisco
70. C. Wohlin, P. Runeson, M. Höst, M.C. Ohlsson, B. Regnell, and A. Wesslén Experimentation in Software Engineering: An Introduction 2000 Kluwer Academic Publishers Norwell, MA, USA
71. S. Wrobel, An algorithm for multi-relational discovery of subgroups, in: Proceedings of the 1st European Symposium on Principles of Data Mining, pp. 78-87.
72. S. Wrobel Relational data mining Relational Data Mining 2001 Springer pp. 74-101
73. T. Xie, S. Thummalapenta, D. Lo, and C. Liu Data mining for software engineering IEEE Computer 42 2009 55 62
74. H. Zhang, and X. Zhang Comments on "Data mining static code attributes to learn defect predictors" IEEE Transactions on Software Engineering 33 2007 635 637
75. T. Zimmermann, R. Premraj, A. Zeller, Predicting defects for eclipse, in: International Workshop on Predictor Models in Software Engineering (PROMISE'07), p. 9.