A Genetic Algorithm for Automated Test Generation for Satellite On-board Image Processing Applications

Ulrike Witteck, Denis Grießbach, Paula Herber


Satellite on-board image processing technologies are subject to extremely strict requirements with respect to reliability and accuracy in hard real-time. In this paper, we address the problem of automatically selecting test cases that are specifically tailored to provoke mission-critical behavior of satellite on-board image processing applications. Because such applications possess large input domains, it is infeasible to exhaustively execute all possible test cases. In particular, because of their complex computations, it is difficult to find specific test cases that provoke mission-critical behavior. To overcome this problem, we define a test approach that is based on a genetic algorithm. The goal is to automatically generate test cases that provoke worst case execution times and inaccurate results of the satellite on-board image processing application. For this purpose, we define a two-criteria fitness function that is novel in the satellite domain. We show the efficiency of our test approach on experimental results from the Fine Guidance System of the ESA medium-class mission PLATO.


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