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Automated Test Generation for Satellite On-Board Image Processing

Witteck, Ulrike (2018) Automated Test Generation for Satellite On-Board Image Processing. Master's, Technical University Berlin.

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Abstract

On-board image processing technologies in the satellite domain are subject to extremely strict requirements with respect to reliability and accuracy in hard realtime. Due to their large input domain, it is infeasible to exhaustively execute all possible test cases. Furthermore, because of their complex computations, it is difficult to find specific test cases that provoke mission-critical behavior. To overcome these problems, we first define a test approach that efficiently and systematically captures the input domain of satellite on-board image processing applications. We present a dedicated partitioning into equivalence classes for each input parameter. As a result, our approach systematically reduces the number of test cases. Moreover, we define novel multidimensional coverage criteria to assess a given test suite for its coverage on the input domain. We present a test generation algorithm that automatically inserts missing test cases into the given test suite based on our multidimensional coverage criteria. This results in a reasonably small test suite that covers the whole input domain of satellite on-board image processing applications. Second, we define a test approach that automatically searches for test cases that are specifically tailored to provoke mission-critical behavior of satellite on-board image processing applications. For that, we present a novel genetic algorithm. We define a two-criteria fitness function that is based on the execution time and mathematical accuracy of the application under test. Therefore, our algorithm automatically selects test cases that provoke worse execution times and inaccurate results of the satellite on-board image processing application. We investigate the efficiency of our approaches on the PLAnetary Transits and Oscillation of stars (PLATO) Fine Guidance System (FGS) algorithm. This is a satellite on board algorithm that calculates the high-precision attitude of the spacecraft. The experimental results show that our first approach efficiently and systematically generates a test suite. This suite completely covers the input domain with respect to our multidimensional coverage criteria. This test suite has a higher error-detection capability than a randomly generated test suite. Therefore, our test approach increases the test efficiency and quality. Furthermore, our genetic algorithm automatically finds test cases that provoke longer execution times and less accurate results when using the generated test suite from the first approach as search space than using a randomly generated test suite. Hence, our genetic approach improves a given test suite to support robustness testing. As a summary, the combination of our approaches increases the efficiency and effectiveness of the test process for satellite image processing applications.

Item URL in elib:https://elib.dlr.de/129355/
Document Type:Thesis (Master's)
Title:Automated Test Generation for Satellite On-Board Image Processing
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Witteck, UlrikeUlrike.Witteck (at) dlr.deUNSPECIFIED
Date:7 September 2018
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Number of Pages:68
Status:Published
Keywords:Image Processing, Software Testing, Input Partitioning, Genetic Algorithms, Satellite Systems
Institution:Technical University Berlin
Department:Software and Embedded Systems Engineering
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space Science and Exploration
DLR - Research area:Raumfahrt
DLR - Program:R EW - Erforschung des Weltraums
DLR - Research theme (Project):R - Project PLATO
Location: Berlin-Adlershof
Institutes and Institutions:Institute of Optical Sensor Systems > Space Instruments
Deposited By: Witteck, Ulrike
Deposited On:30 Sep 2019 08:36
Last Modified:30 Sep 2019 08:36

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