Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Automated test data generation for programs with procedures
ISSTA '96 Proceedings of the 1996 ACM SIGSOFT international symposium on Software testing and analysis
Extreme programming explained: embrace change
Extreme programming explained: embrace change
Genetic algorithms for dynamic test data generation
ASE '97 Proceedings of the 12th international conference on Automated software engineering (formerly: KBSE)
Breeding Software Test Cases with Genetic Algorithms
HICSS '03 Proceedings of the 36th Annual Hawaii International Conference on System Sciences (HICSS'03) - Track 9 - Volume 9
Investigating the performance of genetic algorithm-based software test case generation
HASE'04 Proceedings of the Eighth IEEE international conference on High assurance systems engineering
A model for terrain coverage inspired by ant's alarm pheromones
Proceedings of the 2007 ACM symposium on Applied computing
Deriving evaluation metrics for applicability of genetic algorithms to optimization problems
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Optimisation of software testing using Genetic Algorithm
International Journal of Artificial Intelligence and Soft Computing
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Like in other fields, computer products (applications, hardware, etc.), before being marketed, require some level of testing to verify whether they meet their design and functional specifications -- called functionality test. The general process of performing functionality test consists in the production of a test plan that is then executed by humans or by automated software tools. The main difficulty in this entire process is the definition of such test plan. How can we know what a good sequence (test plan) is? The rule of thumb is to trust on people who understand the workings of the application being tested and who can decide what should be tested. The danger is that experts, due to their over-confidence on their knowledge, may become blind to issues that should otherwise be easy to see. This paper describes a technique based on genetic algorithms that is able to generate good test plans in an unbiased way and with minimum expert interference.