Using genetic algorithms to generate test plans for functionality testing
Proceedings of the 44th annual Southeast regional conference
Detecting buffer overflow via automatic test input data generation
Computers and Operations Research
GA-based multiple paths test data generator
Computers and Operations Research
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
Towards Good Enough Testing: A Cognitive-Oriented Approach Applied to Infotainment Systems
ASE '08 Proceedings of the 2008 23rd IEEE/ACM International Conference on Automated Software Engineering
Optimization of software testing using genetic algorithms
MACMESE'09 Proceedings of the 11th WSEAS international conference on Mathematical and computational methods in science and engineering
Experimental study on GA-based path-oriented test data generation using branch distance
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Test-Suite reduction using genetic algorithm
APPT'05 Proceedings of the 6th international conference on Advanced Parallel Processing Technologies
Systematically evolving configuration parameters for computational intelligence methods
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
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Faulty software is usually costly and possibly life threatening as software becomes an increasingly critical component in a wide variety of systems. Thorough software testing by both developers and dedicated quality assurance staff is one way to uncover flaws. Automated test generation techniques can be used to augment the process, free of the cognitive biases that have been found in human testers. This paper focuses on breeding software test cases using genetic algorithms as part of a software testing cycle. An evolving fitness function that relies on a fossil record of organisms results in interesting search behaviors, based on the concepts of novelty, proximity, and severity. A case study that uses a simple, but widely studied program is used to illustrate the approach. Several visualization techniques are also introduced to analyze particular fossil records, as well as the overall search process.