Using machine learning to refine Category-Partition test specifications and test suites

Information and Software Technology

Volumn 51, Issue 11, 2009, Pages 1551-1564

  Briand, Lionel C ^a   Labiche, Yvan ^b   Bawar, Zaheer ^b   Spido, Nadia Traldi ^b  

^a UNIVERSITY OF OSLO   (Norway)

^b CARLETON UNIVERSITY   (Canada)

Author keywords

Black box testing; Category Partition; Machine learning; Test improvement

Indexed keywords

BLACK BOX TESTING; BLACK BOXES; CATEGORY-PARTITION; COST EFFECTIVE; MACHINE LEARNING; ON-MACHINES; OPEN SOURCE DEVELOPMENT; PARTITION TESTING; SOFTWARE EVOLUTION; TEST IMPROVEMENT; TEST SPECIFICATIONS; TOOL SUPPORT;

EDUCATION; REFINING; ROBOT LEARNING;

TESTING;

EID: 69549105660     PISSN: 09505849     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.infsof.2009.06.006     Document Type: Article

References (37)

1. Ammann P., and Offutt A.J. Introduction to Software Testing (2008), Cambridge University Press
2. G. Ammons, R. Bodik, J.R. Larus, Mining specifications, in: Proceedings of ACM Principles of Programming Languages, 2002, pp. 4-16.
3. Andrews J.H., Briand L.C., Labiche Y., and Namin A.S. Using mutation analysis for assessing and comparing testing coverage criteria. IEEE Transactions on Software Engineering 32 8 (2006) 608-624
4. N. Baskiotis, M. Sebag, M.-C. Gaudel, S. Gouraud, A machine learning approach for statistical software testing, in: Proceedings of International Joint Conference on Artificial Intelligence, 2007, pp. 2274-2279.
5. B. Baudry, F. Fleurey, Y. Le Traon, Improving test suites for efficient fault localization, in: Proc. International Conference on Software Engineering, 2006, pp. 82-91.
6. J.F. Bowring, J.M. Rehg, M.J. Harrold, Active learning for automatic classification of software behavior, in: Proceedings of the ACM International Symposium on Software Testing and Analysis, 2004, pp. 195-205.
7. L.C. Briand, Y. Labiche, Z. Bawar, Using Machine Learning to Refine Black-box Test Specifications and Test Suites, Carleton University, Technical Report SCE-07-05, 2007.
8. L.C. Briand, Y. Labiche, Z. Bawar, Using machine learning to refine black-box test specifications and test suites, in: Proceedings of the International Conference on Quality Software, 2008.
9. L.C. Briand, Y. Labiche, X. Liu, Using machine learning to support debugging with Tarantula, in: Proceedings of International Symposium on Software Reliability Engineering, 2007, pp. 137-146.
10. Chen T.Y., Poon P.-L., Tang S.-F., and Tse T.H. On the identification of categories and choices for specification-based test case generation. Information and Software Technology 46 13 (2004) 887-898
11. Chen T.Y., Poon P.-L., and Tse T.H. A choice relation framework for supporting category-partition test case generation. IEEE Transactions on Software Engineering 29 7 (2003) 577-593
12. W.W. Cohen, Y. Singer, Simple, fast, and effective rule learner, in: Proceedings of AAAI/IAAI, 1999, pp. 335-342.
13. C. Csallner, Y. Smaragdakis, DSD-Crasher: a hybrid analysis tool for bug finding, in: Proceedings of ACM International Symposium on Software Testing and Analysis, 2006, pp. 245-254.
14. Demeyer S., Ducasse S., and Nierstrasz O. Object-Oriented Reengineering Patterns (2003), Morgan Kaufmann
15. Elbaum S., and Diep M. Profiling deployed software assessing strategies and testing opportunities. Transactions on Software Engineering 31 4 (2005) 312-327
16. Ernst M.D., Cockrell J., Griswold W.G., and Notkin D. Dynamically discovering likely program invariants to support program evolution. IEEE Transaction on Software Engineering 27 2 (2001) 1-25
17. P. Francis, D. Leon, M. Minch, A. Podgurski, Tree-based methods for classifying software failures, in: Proceedings of International Symposium on Software Reliability Engineering, 2004, pp. 451-462.
18. Grochtmann M., and Grimm K. Classification trees for partition testing, software testing. Verification and Reliability 3 2 (1993) 63-82
19. Haran M., Karr A., Last M., Orso A., Porter A., Sanil A., and Fouche S. Techniques for classifying executions of deployed software to support software engineering tasks. Transactions on Software Engineering 33 5 (2007) 1-18
20. M. Harder, J. Mellen, M.D. Ernst, Improving test suites via operational abstraction, in: Proceedings of 25th International Conference on Software Engineering, 2003, pp. 60-71.
21. Hartmann J., Vieira M., Foster H., and Ruder A. A UML-Based approach to system testing. Innovations in Systems and Software Engineering 1 1 (2005) 12-24
22. J. Hartmann, M. Vieira, A. Ruder, UML based test generation and execution, in: Proceedings of Workshop on Software Test, Analyses and Verification, 2004.
23. J.A. Jones, M.J. Harrold, Empirical evaluation of the tarantula automatic fault-localization technique, in: Proceedings of International Conference on Automated Software Engineering, 2005, pp. 273-282.
24. Jorgensen P.C. Software Testing: A Craftsman's Approach. second ed. (1995), CRC Press
25. Y.-S. Ma, A.J. Offutt, Y.-R. Kwon, MuJava: a mutation system for Java, in: Proceedings of International Conference on Software Engineering, 2006, pp. 827-830.
26. P. Maki-Asiala, M. Matinlassi, Quality assurance of open source components: integrator point of view, in: Proceedings of Annual International Computer Software and Applications Conference, 2006, pp. 189-194.
27. B. Marick, The Craft of Software Testing, Prentice Hall, 1995.
28. A.P. Mathur, Foundations of Software Testing, Pearson, 2008.
29. M. Michlmayr, F. Hunt, D. Probert, Quality practices and problems in free software projects, in: Proceedings of First International Conference on Open Source Systems, 2005, pp. 24-28.
30. Ostrand T.J., and Balcer M.J. The category-partition method for specifying and generating functional test. Communications of the ACM 31 6 (1988) 676-686
31. C. Pacheco, M.D. Ernst, Eclat: automatic generation and classification of test inputs, in: Proceedings of European Conference on Object-Oriented Programming, LNCS, vol. 3586, 2005, pp. 504-527.
32. Pasquini A., Crespo A., and Matrelle P. Sensitivity of reliability-growth models to operational profiles errors vs testing accuracy. IEEE Transactions on Reliability 45 4 (1996) 531-540
33. J.R. Quinlan, C4.5: Programs for Machine Learning, Morgan Kaufmann, 1993.
34. H. Singh, M. Conrad, S. Sadeghipour, Test case design based on Z and the classification-tree method, in: Proceedings of International Conference on Formal Engineering Methods, 1997, pp. 81-90.
35. C. Willcock, D. Deiß, S. Tobies, S. Keil, F. Engler, S. Schulz, An Introduction to TTCN-3, Wiley, 2005.
36. Witten I.H., and Frank E. Data Mining: Practical Machine Learning Tools and Techniques. second ed. (2005), Morgan Kaufman
37. Zhao L., and Elbaum S. Quality assurance under the open source development model. Journal of Systems and Software 66 1 (2003) 65-75