Computer
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Generating Software Test Data by Evolution
IEEE Transactions on Software Engineering
The Art of Software Testing
Search-based software test data generation: a survey: Research Articles
Software Testing, Verification & Reliability
Automated Unique Input Output Sequence Generation for Conformance Testing of FSMs
The Computer Journal
Introduction to Software Testing
Introduction to Software Testing
Using formal specifications to support testing
ACM Computing Surveys (CSUR)
Aiding Test Case Generation in Temporally Constrained State Based Systems Using Genetic Algorithms
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
A Theoretical and Empirical Study of Search-Based Testing: Local, Global, and Hybrid Search
IEEE Transactions on Software Engineering
Formal testing of timed and probabilistic systems
ICTSS'11 Proceedings of the 23rd IFIP WG 6.1 international conference on Testing software and systems
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Generating test data for formal state based specifications is computationally expensive. In previous work we presented a framework that addressed this issue by representing the test data generation problem as an optimisation problem. In this paper we analyze a communications protocol to illustrate how the test case generation problem can be presented as a search problem and automated. Genetic algorithms (GAs) and random search are used to generate test data and evaluate the approach. GAs show to outperform random search and seem to scale well as the problem size increases. We consider a very simple fitness function that can be used with other evolutionary search techniques and automated test case generation suites.