Optimizing probabilities of real-time test case execution

Model-based test derivation for real-time system has been proven to be a hard problem for exhaustive test suites. Therefore, techniques for real-time testing do not aim to exhaustiveness but Instead respond to particular coverage criteria. Since it Is not feasible to generate complete test suites for real time systems, It IsI very Important that test case are executed In a way that they can achieve the best possible resuIlt As a consequence, It is imperative to Increase the probabilty of success of a test case execution (by ´success´ we actually mean ´the test finds an error´). This work presents a technique to guide the execution of a test case towards a particular objective with the highest possible probability. Thke technique takes as a starting point a model described In terms of an input/output stochastic automata, where input actions are fully controlled by the tester and the occurrece time of output action responds to uniform distributions. Derived test cases are sequences of Inputs and outputs actions. This work discusses several techniques to obtain the optimum times In which the tester must feed the inputs of the test case in order to achieve maxhmum probabilty of success in a test case execution. In particular~, we show this optimization problem Is equivalent to maximizing the sectional volume of a convex polytope when the probabilty distributions Involved are uniform.