Model-Based Optimization of Testing through Reduction of Stimuli
Proceedings of the Twentieth International Joint Conference on Artificial Intelligence. Pages 593-597.
Abstract
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 variables 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.
@inproceedings{ijcai2007p94,
title = {Model-Based Optimization of Testing through Reduction of Stimuli},
author = {Struss, Peter},
booktitle = {Proceedings of the Twentieth International Joint Conference on
Artificial Intelligence, {IJCAI-07}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ramon López de Mántaras},
pages = {593--597},
year = {2007},
url = {https://ijcai.org/proceedings/2007/94},
}
TY - CPAPER TI - Model-Based Optimization of Testing through Reduction of Stimuli AU - Struss, Peter T2 - Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07 PB - International Joint Conferences on Artificial Intelligence PY - 2007 SP - 593 EP - 597 UR - https://ijcai.org/proceedings/2007/94 ER -Copied
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