Empirical evaluation of optimization algorithms when used in goal-oriented automated test data generation techniques

Empirical Software Engineering

Volumn 12, Issue 2, 2007, Pages 183-239

  Xiao, Man ^a   El Attar, Mohamed ^a   Reformat, Marek ^a   Miller, James ^a  

^a UNIVERSITY OF ALBERTA   (Canada)

Author keywords

Empirical results; Empirical software engineering; Optimization techniques; Test data generation

Indexed keywords

DATA ACQUISITION; FINANCE; OPTIMIZATION; PROJECT MANAGEMENT; SOFTWARE TESTING;

EMPIRICAL RESULTS; EMPIRICAL SOFTWARE ENGINEERING; OPTIMIZATION TECHNIQUES; TEST DATA GENERATION;

SOFTWARE ENGINEERING;

EID: 33847722665     PISSN: 13823256     EISSN: 15737616     Source Type: Journal    
DOI: 10.1007/s10664-006-9026-0     Document Type: Article

References (41)

1. Back T (1996) Evolutionary algorithms in theory and practice. Oxford University Press, New York
2. Back T, Hoffmeister F (1991) Extended selection mechanisms in genetic algorithms. Proc. of the Fourth Int. Conf. on Genetic Algorithms 92-99
3. Back T, Fogel DB, Michalewicz Z (eds) (2000) Evolutionary computations I. Institute of Physics Publishing, Bristol
4. Baker JE (1985) Adaptive selection methods for genetic algorithms. Proc. of an International Conference on Genetic Algorithms. Erlbaum, Hillsdale
5. Beizer B (1990) Software testing techniques. Van Nostrand Reinhold, 2nd edn
6. Bird D, Munoz C (1982) Automatic generation of random SB-checking test cases. IBM Syst J 22 (3):229-245
7. Caruana RA, Schaffer JD (1988) Representation and hidden bias: Gray vs. binary coding for genetic algorithms. In: Proceedings of the Fifth International Conference on Machine Learning, Morgan Kaufmann
8. Chang J, Richardson D, Sankar S (1996) Structural specification-based testing with ADL. In Proceedings of ISSTA'96 (International Symposium on Software Testing and Analysis), pp 62-70, San Diego
9. Change K.H, Cross JH, Carlisle WH, Brown DB (1991) A framework for intelligent test data generators. J Intell Robot Syst-Theory and Application
10. Clarke LA (1976) A system to generate test data and symbolically execute programs. IEEE Trans Softw Eng 2(3):215-222
11. Cohen J (1988) Statistical power analysis for the behavioral sciences, 2nd edn. Erlbaum, New Jersey
12. Corana A, Marchesi M, Martini C, Ridella S (1987) Minimizing multimodal functions of continuous variables with the "simulated annealing" algorithm. ACM Trans Math Softw 13(3):262-280
13. De Jong, K (1975) The analysis of the behavior of a class of genetic adaptive systems, Ph.D. Dissertation, Dept, of Computer Science, University of Michigan, Ann Arbor
14. Dowsland KA (1993) Modern heuristic techniques for combinatorial problems. Chapter 2 - simulated annealing, McGraw Hill, pp 20-69
15. Eiben AE, Hinterding R, Michalewicz Z (1999) Parameter control in evolutionary algorithms. IEEE Transactions Computation 3(2):124-139
16. Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison Wesley, New York
17. Greffenstette JJ (1986) Optimization of control parameters for genetic algorithms. IEEE Trans Syst Man Cybern SMC-16 1:122-128
18. Hedges LV, Olkin I (1985) Statistical methods for meta-analysis. Academic, Orlando, FL
19. Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press
20. Howden WW (1997) Symbolic testing and the DISSECT symbolic evaluation system. IEEE Trans Softw Eng 3(4):266-278
21. Janikow CZ, Michalewicz Z (1991) An experimental comparison of binary and floating point representations in genetic algorithms. In: Belew RK, Booker LB (eds) Proceedings of the Fourth International Conference on Genetic Algorithms, Morgan Kaufmann, pp 31-36
22. Jones B, Sthamer H, Eyres D (1995) The automatic generation of software test data sets using adaptive search techniques. Proc. of the 3rd Conference on Software Quality Management 2:435-444
23. Kirkpatrick S, Gelatt CD Jr, Vecchi MP (1983) Optimization by simulated annealing. Science 220 (4598):671-680
24. Koakutsu S, Kang M, Dai WW (1995) Genetic simulated annealing and applications to non-slicing floorplan design. (UCSC-CRL-95-52)
25. Korel B (1990) A dynamic approach of automated test data generation. Conference on Software Maintenance 311-317
26. Korel B (1996) Automated test data generation for programs with procedures. In: Ziel SJ (ed) Proceedings of the 1996 International Symposium on Software Testing and Analysis (ISSTA), pp 209-215
27. McGraw KO, Wong SP (1992) A common language effect-size statistic. Psychol Bull 111:361-365
28. McMinn P (2004) Search-based software test data generation: a survey. Softw Test Verif Reliab 14 (2): 105-156
29. Michael CC, McGraw G, Schatz MA (2001) Generating software test data by evolution. IEEE Trans Softw Eng 27(12):1085-1110
30. Miki M, Hiroyasu T, Ono K (2002) Simulated annealing with advanced adaptive neighborhood. In: Computational intelligence and applications (Proceedings of the 2nd International Workshop on Intelligent Systems Design and Applications: ISDA-02, 113-118 2002
31. Miller J (2004) Statistical, significance testing - a panacea for software technology experiments? J Syst Sofw 73:183-192
32. Mills HD, Dyer MD, Linger RC (1987) Cleanroom software engineering. IEEE Softw 4(5): 19-25
33. Osman AH, Kelly JP (1996) Meta-heuristics: an overview meta-heuristics: Theory & applications. 1-22
34. Ramamoorfhy CV, Ho SF, Chen WT (1976) On the generation of program test data. IEEE Trans Softw Eng 2 (4):293-300
35. Schafi'er JD (1987) Some effects of selection procedures on hyperplane sampling by genetic algorithms. In: Davis L (ed) Genetic algorithms and simulated annealing. Pitman
36. Schaffer JD, Caruana R, Eshelman L, Das R (1989) A study of control parameters affecting online performance of genetic algorithms for function optimization. In: Proceedings of the 3rd International Conference in Genetic Algorithms, Morgan Kaufmann, San Mateo, California, pp 51-60
37. Tassey G (2002) The economics impact of inadequate infrastructure for software testing. National Institute of Standards and Technology report
38. Thevenhod-Fosse P, Waeselynck H (1998) STAIEMATE: applied to statistical software testing. Proc. of the 1998 International Symposium on Software Testing and Analysis
39. Tracy N, Clark J, Mander K (1998) Automated program flaw finding using simulated annealing. Proc. of the International Symposium on Software Testing and Analysis
40. Voas JM, Morell L, Miller KW (1991) Predicting where faults can hide from testing. IEEE Softw 8 (2):41-38
41. Whitley D (1993) (ed) An executable model of a simple genetic algorithm. Foundations of Genetic Algorithms (FOGA 2). Morgan Kaufmann