The impact of input domain reduction on search-based test data generation

6th Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering, ESEC/FSE 2007

Volumn , Issue , 2007, Pages 155-164

  Harman, Mark ^a   Hassoun, Youssef ^a   Lakhotia, Kiran ^a   McMinn, Phil ^b   Wegener, Joachim ^c  

^a KING'S COLLEGE LONDON   (United Kingdom)

^b UNIVERSITY OF SHEFFIELD   (United Kingdom)

^c DAIMLER AG   (Germany)

Author keywords

Automated test data generation; Evolutionary testing; Genetic algorithms; Hill climbing; Input domain reduction; Search space reduction

Indexed keywords

DATA REDUCTION; GENETIC ALGORITHMS; ONLINE SEARCHING; PROBLEM SOLVING; DATA COMMUNICATION SYSTEMS; LEARNING ALGORITHMS; ROBOT LEARNING; SOFTWARE ENGINEERING; SOFTWARE TESTING; TEST FACILITIES; TESTING;

AUTOMATED TEST DATA GENERATION; EVOLUTIONARY TESTING; HILL CLIMBING; INPUT DOMAIN REDUCTION; SEARCH SPACE REDUCTION;

SOFTWARE TESTING; DATA REDUCTION;

EID: 37849020003     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1287624.1287647     Document Type: Conference Paper

References (28)

1. J. E. Baker. Reducing bias and inefficiency in the selection algorithm. In Proceedings of the 2nd International Conference on Genetic Algorithms and their Application, Hillsdale, New Jersey, USA, 1987. Lawrence Erlbaum Associates.
2. B. Beizer. Software Testing Techniques. Van Nostrand Reinhold, 1990.
3. D. W. Binkley and M. Harman. Analysis and visualization of predicate dependence on formal parameters and global variables. IEEE Transactions on Software Engineering, 30(11):715-735, 2004.
4. th IEEE Software Engineering and Knowledge Engineering (SEKE), pages 43-50, Ischia, Italy, 2002.
5. L. C. Briand, Y. Labiche, and M. Shousha. Stress testing real-time systems with genetic algorithms. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2005), pages 1021-1028. ACM, 2005.
6. S. Chen and S. Smith. Improving genetic algorithms by search space reductions. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 1999), pages 135-140. Morgan Kaufmann, 1999.
7. P. Cousot and R. Cousot. Systematic design of program transformation frameworks by abstract interpretation. ACM SIGPLAN Notices, 31(1):178-190, Jan. 2002.
8. J. W. Duran and S. C. Ntafos. An evaluation of random testing. IEEE Transactions on Software Engineering, 10(4):438-444, 1980.
9. M. Harman, C. Fox, R. M. Hierons, L. Hu, S. Danicic, and J. Wegener. VADA: A transformation-based system for variable dependence analysis. In IEEE International Workshop on Source Code Analysis and Manipulation (SCAM 2002), pages 55-64, 2002. IEEE Computer Society Press.
10. M. Harman and P. McMinn. A theoretical & empirical analysis of evolutionary testing and hill climbing for structural test data generation. International Symposim on Software Testing and Analysis (ISSTA 2007), to appear, 2007.
11. J. C. King. Symbolic execution and program testing. Communications of the ACM, 19(7):385-394, 1976.
12. B. Korel. Automated software test data generation. IEEE Transactions on Software Engineering, 16(8):870-879, 1990.
13. N. Mansour and M. Salame. Data generation for path testing. Software Quality Journal, 12(2):121-134, 2004.
14. G. McGraw, C. Michael, and M. Schatz. Generating software test data by evolution. IEEE Transactions on Software Engineering, 27(12):1085-1110, 2001.
15. P. McMinn. Search-based software test data generation: A survey. Software Testing, Verification and Reliability, 14(2):105-156, 2004.
16. P. McMinn. IGUANA: Input generation using automated novel algorithms. A plug and play research tool. Technical Report, Department of Computer Science, University of Sheffield, 2007.
17. P. McMinn, M. Harman, D. Binkley, and P. Tonella. The species per path approach to search-based test data generation. In Proceedings of the International Symposium on Software Testing and Analysis (ISSTA 2006), pages 13-24, 2006. ACM.
18. W. Miller and D. Spooner. Automatic generation of floating-point test data. IEEE Transactions on Software Engineering, 2(3):223-226, 1976.
19. H. Mühlenbein and D. Schlierkamp-Voosen. Predictive models for the breeder genetic algorithm: I. continuous parameter optimization. Evolutionary Computation, 1(1):25-49, 1993.
20. R. Pargas, M. Harrold, and R. Peck. Test-data generation using genetic algorithms. Software Testing, Verification and Reliability, 9(4):263-282, 1999.
21. F. Schmiedle, R. Drechsler, and B. Becker. Exact routing with search space reduction. IEEE Transactions on Computers, 52(6):815-825, 2003.
22. A. Schultz, J. Grefenstette, and K. Jong. Test and evaluation by genetic algorithms. IEEE Expert, 8(5):9-14, 1993.
23. N. Tracey, J. Clark, K. Mander, and J. McDermid. Automated test-data generation for exception conditions. Software Practice and Experience, 30(1):61-79, 2000.
24. J. Wegener, A. Baresel, and H. Sthamer. Evolutionary test environment for automatic structural testing. Information and Software Technology, 43(14):841-854, 2001.
25. J. Wegener and O. Bühler. Evaluation of different fitness functions for the evolutionary testing of an autonomous parking system. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2004), Lecture Notes in Computer Science vol. 3103, pages 1021-1028, Springer-Verlag, 2004.
26. M. Weiser. Program slicing. IEEE Transactions on Software Engineering, 10(4):352-357, 1984.
27. D. Whitley. The GENITOR algorithm and selection pressure: Why rank-based allocation of reproductive trials is best. In J. D. Schaffer, editor, Proceedings of the 3rd International Conference on Genetic Algorithms, pages 116-121, San Mateo, California, USA, 1989. Morgan Kaufmann.
28. S. Xanthakis, C. Ellis, C. Skourlas, A. Le Gall, S. Katsikas, and K. Karapoulios. Application of genetic algorithms to software testing (Application des algorithmes génétiques au test des logiciels). In 5th International Conference on Software Engineering and its Applications, pages 625-636, Toulouse, France, 1992.