Model-based optimization of testing through reduction of stimuli

The paper presents the theoretical foundations and an algorithm to reduce the efforts of testing physical systems. A test is formally described as a set of stimuli (inputs to the system) to shift the system into a particular situation or state, and a set of varia-bles whose observation or measurement refutes hypotheses about the behavior mode the system is operating in. Tests (either generated automatically or by humans) may contain redundancy in the sense that some of its stimuli and/or observables maybe irrelevant for achieving the result of the test. Identifying and dropping them contributes to redu-cing the cost of set-up actions and measurements. We define different kinds of irrelevant stimuli, discuss their practical importance, and present criteria and algorithms for computing reduced tests.