Integrating in-process software defect prediction with association mining to discover defect pattern

Information and Software Technology

Volumn 51, Issue 2, 2009, Pages 375-384

  Chang, Ching Pao ^a   Chu, Chih Ping ^a   Yeh, Yu Fang ^b  

^a NATIONAL CHENG KUNG UNIVERSITY   (Taiwan)

^b NATIONAL CHANGHUA UNIVERSITY OF EDUCATION   (Taiwan)

Author keywords

Association rule; Multi interval discretization; Software defect prediction

Indexed keywords

ASSOCIATION RULES; ASSOCIATIVE PROCESSING; DATA MINING; DECISION THEORY; DECISION TREES; FORECASTING; MATHEMATICAL MODELS; MINING;

ASSOCIATION MININGS; ASSOCIATION RULE MININGS; BASIC OPERATIONS; CAUSAL ANALYSIS; CONTINUOUS ATTRIBUTES; CORRECTIVE ACTIONS; DECISION TREE TECHNIQUES; DEFECT PREDICTIONS; DEFECT PREVENTIONS; DISCRETIZATION; EARLY STAGES; MINIMUM SUPPORTS; MULTI-INTERVAL DISCRETIZATION; PREDICTION MODELS; PROCESS PROJECTS; PROCESS SOFTWARES; RULE SETS; SOFTWARE DEFECT PREDICTION; SOFTWARE PROCESS IMPROVEMENTS; SOFTWARE PROCESSES; SOFTWARE PROJECTS; TIME CONSUMING;

DEFECTS;

EID: 56349170658     PISSN: 09505849     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.infsof.2008.04.008     Document Type: Article

References (47)

1. Mohapatra S., and Mohanty B. Defect prevention through defect prediction: a case study at Infosys. IEEE International Conference on Software Maintenance (2001) 206-272
2. Mays R.G., Jones C.L., Holloway G.J., and Studinski D.P. Experiences with defect prevention. IBM System Journal 29 (1990) 4-32
3. Chillarege R., Bhandari I.S., Chaar J.K., Halliday M.J., Moebus D.S., Ray B.K., and Wong M.-Y. Orthogonal defect classification - a concept for in-process measurements. IEEE Transactions on Software Engineering 18 (1992) 943-956
4. Leszak M., Perry D.E., and Stoll D. Classification and evaluation of defects in a project retrospective. The Journal of System and Software 61 (2002) 173-187
5. Khoshgoftaar T.M., Allen E.B., Jones W.D., and Hudepohl J.P. Classification-tree models of software-quality over multiple releases. IEEE Transactions on Reliability 49 (2000) 4-11
6. Zhong S., Khoshgoftaar T.M., and Seliya N. Analyzing software measurement data with clustering techniques. IEEE Intelligent System 19 (2004) 20-27
7. Graves T.L., Karr A.F., Marron J.S., and Siy H. Predicting fault incidence using software change history. IEEE Transactions on Software Engineering 26 (2000) 653-661
8. Chang C.-P., and Chu C.-P. Defect prevention in software processes: an action-based approach. Journal of Systems and Software 80 4 (2007) 559-570
9. Liu B., Ma Y., Wong C.-K., and Yu P.S. Scoring the data using association rules. Applied Intelligence 18 2 (2003) 119-135
10. Westphal C., and Blaxton T. Data Mining Solutions: Methods and Tools for Solving Real-World Problems (1998), John Wiley & Sons
11. Hand D., Mannila H., and Smyth P. Principles of Data Mining (2001), MIT Press, Cambridge, MA
12. Weiss G.M. Mining with rarity: a unifying framework. ACM SIGKDD Explorations Newsletter 6 (2004) 7-19
13. Hall M.A., and Holmes G. Benchmarking attribute selection techniques for discrete class data mining. IEEE Transaction on Knowledge and Data Engineering 15 3 (2003) 1437-1447
14. A.A. Freitas, S.H. Lavington, Speeding up knowledge discovery in large relational database by means of a new discretization algorithm, in: Proceedings of 14th British Nat. Conf. on Database, Edinburgh, UK, 1996, pp. 124-133.
15. Pressman R.S. Software Engineering: A Practitioner's Approach (2001), McGraw-Hill, NJ
16. Boehm B., and Basili V. Software defect top 10 lists. IEEE Computer 34 1 (2001) 2-6
17. Emam K.E., and Laitenberger O. Evaluating capture-recapture models with two inspectors. IEEE Transactions Software Engineering 27 (2001) 851-864
18. Ohlsson M.C., Amschler Andrews A., and Wohlin C. Modelling fault-proneness statistically over a sequence of releases: a case study. Journal of Software Maintenance 13 3 (2001) 167-199
19. Chang C.-P., Chu C.-P., and Yeh Y.-F. Time approach for estimating the defect number. Journal of Software Engineering Studies 2 1 (2007) 138-150
20. Thet Thwin M.M., and Quah T.-S. Application of neural networks for software quality prediction using object-oriented metrics. The Journal of Systems and Software 76 (2005) 147-156
21. Khoshgoftaar T.M., and Lanning D.L. A neural network approach for early detection of program modules having high risk in the maintenance phase. Journal of Systems and Software 29 1 (1995) 85-91
22. Bibi S., Tsoumakas G., Stamelos I., and Vlahavas I. Regression via classification applied on software defect estimation. Expert Systems with Applications 34 (2008) 2091-2101
23. Graves T., Karr J.A., and Marron H.S. Predicting fault incidence using software change history. IEEE Transactions on Software Engineering 26 7 (2000) 653-661
24. Salem A.M., Rekabb K., and Whittakerc J.A. Prediction of software failures through logistic regression. Information and Software Technology 46 (2004) 519-523
25. Fenton N., Neil M., Marsh W., Hearty P., Marquez D., Krause P., and Mishra R. Predicting software defects in varying development lifecycles using Bayesian nets. Information and Software Technology 49 (2007) 32-43
26. W. Klösgen, J.M. Zytkow, Knowledge discovery in databases terminology, Advances in Knowledge Discovery and Data Mining, AAAI Press/The MIT Press, Cambridge, MA, 1996.
27. Quinlan J.R. C4.5: Programs for Machine Learning (1993), Morgan Kaufman Publishers, USA
28. Huntley C.L. Organizational learning in open-source software projects: an analysis of debugging data. IEEE Transactions on Engineering Management 50 4 (2003) 485-493
29. Lutz R.R., and Mikulski I.C. Operational anomalies as a cause of safety-critical requirements evolution. Journal of Systems and Software 65 2 (2003) 155-161
30. Pighin M. An empirical quality measure based on complexity values. Information and Software Technology 40 14 (1998) 861-864
31. Munson J.C., Nikora A.P., and Sherif J.S. Software faults: a quantifiable definition. Advances in Engineering Software 37 5 (2006) 327-333
32. Lawler J., and Kitchenham B. Measurement modeling technology. IEEE Software 12 3 (2003) 68-75
33. Cusumano M. Japan's Software Factories: A Challenge to U.S. Management (1991), Oxford University Press, New York
34. Han J., and Kamber M. Data Mining: Concepts and Techniques (2000), Morgan Kaufmann, San Francisco, CA
35. P. Perner, S. Trautzsch, Multi-interval discretization methods for decision tree learning, in: Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition, 1998, pp. 475-482.
36. Japkowicz N., and Stephan S. The class imbalance problem: a systematic study. Intelligent Data Analysis 6 5 (2002) 429-449
37. Michalak K., and Kwaśnicka H. Correlation-based feature selection stragegy in classification problems. Internation Journal of Applied Mathematics and Computer Science 16 4 (2006) 503-511
38. Rajeev R., and Shim K. A decision tree classifier that integrates building and pruning. Data Mining and Knowledge Discovery 4 4 (2000) 315-344
39. Li J., Shen H., and Topor R. Mining informative rule set for prediction. Journal of Intelligent Information Systems 22 2 (2004) 155-174
40. Basile T.M.A., Esposito F., Mauro N.D., and Ferilli S. Handling continuous-valued attributes in incremental first-order rules learning. Lecture Notes in Computer Science 3673 (2005) 430-441
41. Boulle M. Khiops: a statistical discretization method of continuous attributes. Machine Learning 55 1 (2004) 53-69
42. Tay F.E.H. A modified chi2 algorithm for discretization. IEEE Transaction on Knowledge and Data Engineering 14 3 (2002) 666-670
43. Hwang L.S.G.J., and Li F. A dynamic method for discretization of continuous attributes. Lecture Notes in Computer Science 2412 (2002) 181-193
44. R. Srikant, R. Agrawal, Mining quantitative association rules in large relational tables, in: Proceeding of the international conf. on Management of data, 1996, pp. 1-12.
45. Liu B., Hsu W., Chen S., and Ma Y. Analyzing the subjective interestingness of association rules. IEEE Intelligent Systems 15 5 (2000) 47-55
46. Rastogi R., and Shim K. Mining optimized association rules with categorical and numeric attributes. IEEE Transactions on Knowledge and Data Engineering 14 1 (2002) 1041-4347
47. K. Wang, S.H.W. Tay, B. Liu, Interestingness-based interval merger for numeric association rules, in: Proceedings of the 4th Intl. Conf. on Knowledge Discovery and Data Mining (KDD'98), New York, 1998, pp. 121-128.