Using data mining for automated software testing

International Journal of Software Engineering and Knowledge Engineering

Volumn 14, Issue 4, 2004, Pages 369-393

  Last, M ^a   Friendman, M ^a,b   Kandel, A ^c,d  

^a BEN GURION UNIVERSITY OF THE NEGEV   (Israel)

^b NUCLEAR RESEARCH CENTER NEGEV   (Israel)

^c University of South Florida   (United States)

^d TEL AVIV UNIVERSITY   (Israel)

Author keywords

Automated software testing; Data mining; Finite element solver; Info fuzzy networks; Input output analysis; Regression testing

Indexed keywords

ALGORITHMS; AUTOMATION; DATA MINING; FINITE ELEMENT METHOD; NEURAL NETWORKS; PLANNING; REGRESSION ANALYSIS;

AUTOMATED SOFTWARE TESTING; FINITE ELEMENT SOLVERS; INFO-FUZZY NETWORKS (IFN); INPUT-OUTPUT ANALYSIS; REGRESSION TESTING;

COMPUTER SOFTWARE;

EID: 4644343548     PISSN: 02181940     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0218194004001737     Document Type: Conference Paper

References (43)

1. Astra QuickTest from Mercury Interactive, http://astratryandbuy. mercuryinteractive.com
2. B. Beizer, Software Testing Techniques. 2nd Edition (Thomson, 1990).
3. M. R. Blackburn, R. D. Busser, and J. S. Fontaine, Automatic generation of test vectors for SCR-style specifications, in Proc. 12th Ann. Conf. on Computer Assurance (Gaithersburg, Maryland, June 1997).
4. L. Breiman, J. H. Friedman, R. A. Olshen, and P. J. Stone, Classification and Regression Trees (Wadsworth, 1984).
5. R. E. Bryant, Graph-based algorithms for Boolean function manipulation, IEEE Trans. on Computers C-35-8 (1986) 677-691.
6. T. M. Cover and J. A. Thomas, Elements of Information Theory (Wiley, 1991).
7. R. A. DeMillo and A. J. Offlut, Constraint-based automatic test data generation, IEEE Trans. on Software Engineering 17(9) (1991) 900-910.
8. E. Dustin, J. Rashka, and J. Paul, Automated Software Testing: Introduction, Management, and Performance (Addison-Wesley, 1999).
9. S. Elbaum, A. G. Malishevsky, and G. Rothermel, Prioritizing test cases for regression testing, in Proc. ISSTA '00, 2000, pp. 102-112.
10. M. El-Ramly, E. Stroulia, and P. Sorenson, From run-time behavior to usage scenarios: An interaction-pattern mining approach, in Proc. KDD-2002, ACM Press, pp. 315-327.
11. U. Fayyad and K. Irani, Multi-interval discretization of continuous-valued attributes for classification learning, in Proc. Thirteenth Int'l Joint Conf. on Artificial Intelligence, San Mateo, CA, 1993, pp. 1022-1027.
12. M. Friedman, Y. Rosenfeld, A. Rabinovitch, and R. Thieberger, Finite element methods for solving the two-dimensional Schrödinger equation, J. Computational Physics 26(2) (1978).
13. M. Friedman, Y. Rosenfeld, A. Rabinovitch, and R. Thieberger, Thomas-Fermi equation with non-spherical boundary conditions. Finite element methods for solving, J. Computational Physics 70(2) (1987).
14. M. Friedman, M. Schneider, and A. Kandel, FIDES - Fuzzy Intelligent Differential Equation Solver (Avignon, 1987).
15. M. Friedman, M. Strauss, P. Amendt, R. A. London and M. E. Glinsky, Two-dimensional Rayleigh model for bubble evolution in soft tissue, Physics of Fluids 14(5) (2002).
16. D. Hamlet, What Can We Learn by Testing a Program? in Proc. ISSTA 98 (1998) 50-52.
17. J. Han and M. Kamber, Data Mining: Concepts and Techniques (Morgan Kaufmann, 2001).
18. R. Hildebrandt and A. Zeller, Simplifying failure-inducing input, in Proc. ISSTA '00, 2000, pp. 135-145.
19. C. Kaner, J. Falk, and H. Q. Nguyen, Testing Computer Software (Wiley, 1999).
20. R. Kohavi, Bottom-up induction of oblivious read-once decision graphs, in Proc. ECML-94, European Conference on Machine Learning, Catania, Italy, pp. 154-169.
21. R. Kohavi and C.-H. Li, Oblivious decision trees, graphs, and top-down pruning, in Proc. Int. Joint Conf. on Artificial Intelligence (IJCAI), 1995, pp. 1071-1077.
22. M. Last and A. Kandel, Automated quality assurance of continuous data, in Systematic Organization of Information in Fuzzy Systems, eds. P. Melo-Pinto, H.-N. Teodorescu, and T. Fukuda (IOS Press, NATO ASI Series, 2003), pp. 89-104.
23. M. Last and A. Kandel, Automated test reduction using an info-fuzzy network, in Software Engineering with Computational Intelligence, ed. T. M. Khoshgoftaar (Kluwer Academic Publishers, 2003), pp. 235-258.
24. M. Last and O. Maimon, A compact and accurate model for classification. IEEE Trans. on Knowledge and Data Engineering 18(2) (2004) 203-215.
25. M. Last, Online classification of nonstationary data streams, Intelligent Data Analysis 6(2) (2002) 129-147.
26. M. Last, A. Kandel, and O. Maimon, Information-theoretic algorithm for feature selection, Pattern Recognition Letters 22(6-7) (2001) 799-811.
27. M. Last, O. Maimon, and E. Minkov, Improving stability of decision trees, Int. J. Pattern Recognition and Artificial Intelligence 18(2) (2002) 145-159.
28. O. Maimon and M. Last, Knowledge Discovery and Data Mining - The Info-Fuzzy Network (IFN) Methodology (Kluwer Academic Publishers, 2000).
29. A. von Mayrhauser, C. W. Anderson, T. Chen, R. Mraz, and C. A. Gideon, On the promise of neural networks to support software testing, in Computational Intelligence in Software Engineering, eds. W. Pedrycz and J. F. Peters (World Scientific, 1998), pp. 3-32.
30. B. H. McDonald and A. Wexler, Finite element solution of unbounded field problems, IEEE Trans. on Microwave Theory and Techniques MTT-20(12) (1972).
31. S. G. Mikhlin, Variational Methods in Mathematical Physics (Pergamon Press, Oxford, 1965).
32. E. W. Minium, R. B. Clarke, and T. Coladarci, Elements of Statistical Reasoning (Wiley, New York, 1999).
33. T. M. Mitchell, Machine Learning (McGraw-Hill, 1997).
34. S. Nahamias, Production and Operations Analysis, 2nd ed. (Irwin, 1993).
35. National Institute of Standards & Technology, The Economic Impacts of Inadequate Infrastructure for Software Testing, Planning Report 02-3 (May 2002).
36. S. L. Pfleeger, Software Engineering: Theory and Practice, 2nd ed. (Prentice-Hall, 2001).
37. J. R. Quinlan, C4-5: Programs for Machine Learning (Morgan Kaufmann, 1993).
38. C. R. Rao and H. Toutenburg, Linear Models: Least Squares and Alternatives (Springer-Verlag, 1995).
39. P. J. Schroeder and B. Korel, Black-box test reduction using input-output analysis, in Proc. ISSTA '00, 2000, pp. 173-177.
40. M. Vanmali, M. Last, and A. Kandel, Using a neural network in the software testing process, Int. J. Intelligent Systems 17(1) (2002) 45-62.
41. J. M. Voas and G. McGraw, Software Fault Injection: Inoculating Programs against Errors (Wiley, 1998).
42. E. Weyuker, T. Goradia, and A. Singh, Automatically generating test data from a Boolean specification, IEEE Transactions on Software Engineering 20(5) (1994) 353-363.
43. O. C. Zienkiewicz, The Finite Element Method in Engineering Science (McGraw-Hill, London, 1971).