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Impact of mutation intensity on evolutionary test model learning

Automation in the software testing process has significant impact on the overall software development in industry. The focus of this paper is on automation of test case design via model-based testing for automotive embedded software. A new method based on an evolutionary algorithm for acquiring the necessary test model automatically from sample test cases and additional sources of information was designed and this paper investigates the impact of mutation intensity on the evolutionary learning process.