Optimizing Test Input Generation for Reactive Systems with an Adaptive Differential Evolution

The development of search-based algorithms for automatic test case generation is a key issue in the research area of software testing. Evolutionary algorithms have been frequently used for this purpose due to their ability to solve complex optimization problems. In this paper we introduce a novel approach to the automatic test-case generation problem for reactive software systems. We build upon our previous work where we defined a test generation framework based on parameterized executable environment models written in the Lutin language. The main contribution of this paper is the application of a self-adaptive evolutionary algorithm, JADE in the context of our test generation framework and the evaluation of its performance on a realistic reactive system written in Scade. Our preliminary results show that adaptive differential evolution can be used efficiently to increase the structural coverage of the system under test and is easier to use due to the fewer parameters that require fine-tuning.