Using Design of Experiments to Optimise a Decision of Sufficient Testing

Testing of safety-critical embedded systems is an important and costly endeavor. To date researchers and practitioners have been mainly focusing on the design and application of diverse testing strategies, but leaving the test stopping criteria as an ad hoc decision and an open research issue. In our previous work, we proposed a convergence algorithm that informs the tester when the current testing strategy does not seem to be revealing new insight into the worst-case timing properties of tasks and hence should be stopped. This algorithm was shown to be successful but its trial and error tuning of parameters was an issue. In this paper, we use the Design of Experiment (DOE) approach to optimise the algorithm's performance and to improve its scalability. During our experimental evaluations the optimised algorithm showed improved performance by achieving relatively the same results with 42% less testing cost as compared to our previous work. The algorithm also has better scalability and opens up a new path towards achieving cost effective non-functional testing of real-time embedded systems.