Transition coverage testing for simulink/stateflow models using messy genetic algorithms
Proceedings of the 13th annual conference on Genetic and evolutionary computation
An improved test generation approach from extended finite state machines using genetic algorithms
SEFM'12 Proceedings of the 10th international conference on Software Engineering and Formal Methods
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Experimental work in software testing has generally focused on evaluating the effectiveness and efficiency on various source code programs. However, an important issue of testing efficiency on the model level has not been sufficiently addressed, and hitherto, no empirical studies exist. This paper presents an automated test data generation system for feasible transition paths (FTP) on Extended Finite State Machines (EFSM) models and investigates the statistical properties of testing efficiency using statistical tests for correlation and formalisation according to the test data generated by applying the system on four widely used EFSM models. An important and encouraging finding is a close positive correlation between test generation cost and the number of numerical equal operators in conditions (NNEOC) on a FTP. In addition, as the NNEOC increases, there is a raising correlation between the test generation cost and the length of path with events variables (LPEV) or the number of numerical event variables on a path (NNEV), and NNEV increases linearly with the LPEV. Furthermore, empirical study shows that there is very strong exponential relationship between test generation cost and NNEV or LPEV only when NNEOC is considerable. The results provide a significant guide to predict the testing efficiency for EFSM models.