On the performance of estimation of distribution algorithms applied to software testing

Applied Artificial Intelligence

Volumn 19, Issue 5, 2005, Pages 457-489

  Sagarna, Ramón ^a,b   Lozano, Jose A ^a  

^a UNIVERSITY OF THE BASQUE COUNTRY UPV EHU   (Spain)

^b NONE   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; AUTOMATION; EVOLUTIONARY ALGORITHMS; OPTIMIZATION; PARAMETER ESTIMATION; PERFORMANCE; PROBLEM SOLVING; PROJECT MANAGEMENT; TESTING;

BRANCH COVERAGE CRITERIA; SEARCH SPACE SIZE; SOFTWARE TESTING; TEST DATA GENERATION;

COMPUTER SOFTWARE;

EID: 17844361963     PISSN: 08839514     EISSN: None     Source Type: Journal    
DOI: 10.1080/08839510590917861     Document Type: Article

References (40)

1. Bäck, T. 1996. Evolutionary Algorithms in Theory and Practice. Oxford: Oxford University Press.
2. Baluja, S. 1994. Population-based incremental learning: A method for integrating genetic search-based function optimization and competitive learning. Technical Report CMU-CS-94-163. Carnegie Mellon University, Pittsburgh.
3. Baluja, S. and S. Davies. 1997. Combining multiple optimization with optimal dependency trees. Technical Report CMU-CS-97-157. Carnegie Mellon University, Pittsburgh.
4. Beizer, B. 1990. Software Testing Techniques. New York, NY: Van Nostrand Rheinhold.
5. Boehm, B. W. 1981. Software Engineering Economics. Englewood Cliffs, NJ: Prentice Hall.
6. Burton, S. 2000. Automated testing from Z specifications. Technical Report YCS329. Department of Computer Science, University of York, Heslington, York.
7. Castillo, E., J. Gutiérrez, and A. Hadi. 1997. Expert Systems and Probabilistic Network Models. New York, NY: Springer-Verlag.
8. Chow, C. and C. Liu. 1968. Approximating discrete probability distributions with dependence trees. IEEE Transactions on Information Theory. 14(3):462-467.
9. Cooper, G. F. and E. A. Herskovits. 1992. A Bayesian method for the induction of probabilistic networks from data. Machine Learning. 9(4):309-347.
10. Corno, F., P. Prinetto, M. Rebaudengo, and M. Sonza-Reorda. 1996. Exploiting competing subpopulations for automatic generation of test sequences for digital circuits. In Proceedings of the Fourth International Conference on Parallel Problem Solving from Nature, eds. H. M. Voigt, W. Ebeling, I. Rechenberger, and H. P. Schwefel, pages 792-800, Springer-Verlag.
11. De Bonet, J. S., C. L. Isbell, and P. Viola. 1997. MIMIC: Finding optima by estimating probability densities. In Advances in Neural Information Processing Systems 9, eds. M. I. Jordan, M. C. Mozer, and T. Petsche, 424-430, Cambridge, MA: The MIT Press.
12. Demillo, R. and A. Offut. 1993. Experimental results from an automatic test case generator. ACM. Transactions on Software Engineering and Methodology. 2(2):109-127.
13. Duran, J. W. and S. C. Ntafos. 1984. An evaluation of random testing. IEEE Transactions on Software Engineering 10(4):438-444.
14. Fenton, N. E. 1985. The structural complexity of flowgraphs. In Graph Theory with Applications to Algorithms and Computer Science, eds. Y. Alavi, G. Chartrand, L. Lesniak, D. R. Lick, and C. E. Wall, 273-282, New York, NY: John Wiley & Sons.
15. Glover, F. and M. Laguna. 1997. Tabu Search. Boston, MA: Kluwer Academic Publishers.
16. Goldberg, D. E. 1989. Genetic Algorithms in Search, Optimization and Machine Learning. Reading, MA: Addison-Wesley.
17. González, C., J. A. Lozano, and P. Larrañaga. 2000. Analyzing the population-based incremental learning algorithm by means of discrete dynamical systems. Complex Systems. 12(4):465-479.
18. Grefenstette, J. J. 1986. Optimization of control parameters for genetic algorithms. IEEE Transactions on Systems, Man, and Cybernetics. 16(1):122-128.
19. Henrion, M. 1988. Propagating uncertainty in Bayesian networks by probabilistic logic sampling. In Proceedings of the Second Conference on Uncertainty in Artificial Intelligence., eds. J. F. Lemmer, and L. N. Kanal, pages 149-163, North-Holland.
20. Inza, I., P. Larrañaga, R. Etxeberria, and B. Sierra. 2000. Feature subset selection by Bayesian networks-based optimization. Artificial Intelligence. 123(1-2):157-184.
21. Jones, B., H. Sthamer, and D. Eyres. 1996. Automatic structural testing using genetic algorithms. Software Engineering Journal 11(5):299-306.
22. Kirkpatrick, S., C. D. Gellat, and M. P. Vecchi. 1983. Optimization by simulated annealing. Science. 220 (4598):671-680.
23. Korel, B. 1990. Automated software test data generation. IEEE Transactions on Software Engineering. 16(8):870-879.
24. Larrañaga, P., R. Etxeberria, J. A. Lozano, and J. M. Peña. 2000. Combinatorial optimization by learning and simulation of Bayesian networks. In Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence, eds. C. Boutilier, and M. Goldszmidt, pages 343-352, Morgan Kaufmann.
25. Larrañaga, P. and J. A. Lozano. 2002. Estimation of Distribution Algorithms. A New Tool for Evolutionary Computation. Boston, MA: Kluwer Academic Publishers.
26. McGraw, G., C. Michael, and M. Schatz. 1998. Generating software test data by evolution. Technical Report RSTR-018-97-01, RST Corporation, Sterling, Virginia, USA.
27. Mühlenbein, H. 1998. The equation for response to selection and its use for prediction. Evolutionary Computation. 5(3):303-346.
28. Mühlenbein, H. and T. Mahnig. 2002. Evolutionary optimization and the estimation of search distributions with applications to graph bipartitioning. Journal of Approximate Reasoning. 31 (3):157-192.
29. Mühlenbein, H. and G. Paaß. 1996. From recombination of genes to the estimation of distributions I. Binary parameters. In Proceedings of the Fourth International Conference on Parallel Problem Solving from Nature, eds. H. M. Voigt, W. Ebeling, I. Rechenberger, and H. P. Schwefel, pages 178-187, Springer-Verlag.
30. Ould, M. 1991. Testing-A challenge to method and tool developers. Software Engineering Journal. 6(2):59-64.
31. Pargas, R., M. Harrold, and R. Peck. 1999. Test-data generation using genetic algorithms. Journal of Software Testing, Verification and Reliability. 9(4):263-282.
32. Pelikan, M., D. E. Goldberg, and F. Lobo. 1999. A survey of optimization by building and using probabilistic models. Technical Report IlliGAL Report 99018. University of Illinois at Urbana-Champaing, Urbana.
33. Pohlheim, H. and J. Wegener. 1999. Testing the Temporal behavior of real-time software modules using extended evolutionary algorithms. In Proceedings of the Second Genetic and Evolutionary Computation Conference, eds. W. Banzhaf, J. Daida, A. E. Eiben, M. H. Garzon, V. Honavar, M. Jakiela, and R. E. Smith, pages 1795-1802, Morgan Kaufmann.
34. Roper, M., I. Maclean, A. Brooks, J. Miller, and M. Wood. 1995. Genetic algorithms and the automatic generation of test data. Technical Report EFoCS-19-95. University of Stratchclyde, Glasgow.
35. Schwarz, G. 1978. Estimating the dimension of a model. Annals of Statistics. 7(2):461-464.
36. Shepperd, M. 1993. Software Engineering Metrics I: Measures and Validations. New York: McGraw-Hill.
37. Smith, J. and T. C. Fogarty. 1996. Evolving software test data - GA's learn self expression. In Proceedings of Evolutionary Computing. AISB Workshop, ed. T. C. Fogarty, pages 346-354, Springer-Verlag.
38. Sthamer, H. 1996. The Automatic. Generation of Software Test Data Using Genetic Algorithms. Ph.D. Thesis. University of Glamorgan, Pontyprid, Wales, Great Britain.
39. Tracey, N., J. Clark, K. Mander, and J. McDermid. 1998. An automated framework for structural test-data generation. In Proceedings of the 13th IEEE Conference on Automated Software Engineering, eds. D. Redmiles, and B. Nuseibeh, pages 285-288, IEEE CS Press.
40. Wegener, J., A. Baresel, and H. Sthamer. 2001. Evolutionary test environment for automatic structural testing. Information and Software. Technology. 43(14):841-854.