Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
ACM Transactions on Mathematical Software (TOMS)
Software testing techniques (2nd ed.)
Software testing techniques (2nd ed.)
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
STATEMATE applied to statistical software testing
ISSTA '93 Proceedings of the 1993 ACM SIGSOFT international symposium on Software testing and analysis
Modern heuristic techniques for combinatorial problems
Structural specification-based testing with ADL
ISSTA '96 Proceedings of the 1996 ACM SIGSOFT international symposium on Software testing and analysis
Automated test data generation for programs with procedures
ISSTA '96 Proceedings of the 1996 ACM SIGSOFT international symposium on Software testing and analysis
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Automated program flaw finding using simulated annealing
Proceedings of the 1998 ACM SIGSOFT international symposium on Software testing and analysis
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Predicting Where Faults Can Hide from Testing
IEEE Software
Generating Software Test Data by Evolution
IEEE Transactions on Software Engineering
Adaptive Selection Methods for Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Simulated annealing with advanced adaptive neighborhood
Second international workshop on Intelligent systems design and application
GENETIC SIMULATED ANNEALING AND APPLICATION TO NON-SLICING FLOORPLAN DESIGN
GENETIC SIMULATED ANNEALING AND APPLICATION TO NON-SLICING FLOORPLAN DESIGN
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Statistical significance testing: a panacea for software technology experiments?
Journal of Systems and Software - Special issue: Applications of statistics in software engineering
Search-based software test data generation: a survey: Research Articles
Software Testing, Verification & Reliability
Symbolic Testing and the DISSECT Symbolic Evaluation System
IEEE Transactions on Software Engineering
Automatic generation of random self-checking test cases
IBM Systems Journal
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Proceedings of the 2007 international symposium on Software testing and analysis
A multi-objective approach to search-based test data generation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Search based software testing of object-oriented containers
Information Sciences: an International Journal
Empirical analysis of a genetic algorithm-based stress test technique
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Automated test data generation using a scatter search approach
Information and Software Technology
Evolutionary algorithms for the multi-objective test data generation problem
Software—Practice & Experience
Achieving scalable model-based testing through test case diversity
ACM Transactions on Software Engineering and Methodology (TOSEM)
Generating test data for both path coverage and fault detection using genetic algorithms
Frontiers of Computer Science: Selected Publications from Chinese Universities
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Software testing is an essential process in software development. Software testing is very costly, often consuming half the financial resources assigned to a project. The most laborious part of software testing is the generation of test-data. Currently, this process is principally a manual process. Hence, the automation of test-data generation can significantly cut the total cost of software testing and the software development cycle in general. A number of automated test-data generation approaches have already been explored. This paper highlights the goal-oriented approach as a promising approach to devise automated test-data generators. A range of optimization techniques can be used within these goal-oriented test-data generators, and their respective characteristics, when applied to these situations remain relatively unexplored. Therefore, in this paper, a comparative study about the effectiveness of the most commonly used optimization techniques is conducted.