Reference : MASS: A tool for Mutation Analysis of Space CPS
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
Security, Reliability and Trust
http://hdl.handle.net/10993/50092
MASS: A tool for Mutation Analysis of Space CPS
English
Cornejo Olivares, Oscar Eduardo mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SVV >]
Pastore, Fabrizio mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SVV >]
Briand, Lionel mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SVV >]
May-2022
2022 IEEE/ACM 44st International Conference on Software Engineering
Yes
International Conference on Software Engineering
from 22-05-2022 to 27-05-2022
[en] mutation analyisis ; cps ; European space agency
[en] We present MASS, a mutation analysis tool for embedded software in cyber-physical systems (CPS). We target space CPS (e.g., satellites) and other CPS with similar characteristics (e.g., UAV).
Mutation analysis measures the quality of test suites in terms of the percentage of detected artificial faults. There are many mutation analysis tools available, but they are inapplicable to CPS because of scalability and accuracy challenges.
To overcome such limitations, MASS implements a set of optimization techniques that enable the applicability of mutation analysis and address scalability and accuracy in the CPS context. MASS has been successfully evaluated on a large study involving embedded software systems provided by industry partners; the study includes an on-board software system managing a microsatellite currently on-orbit, a set of libraries used in deployed cubesats, and a mathematical library provided by the European Space Agency. A demo video of MASS is available at https://www.youtube.com/watch?v=gC1x9cU0-tU.
http://hdl.handle.net/10993/50092
https://www.youtube.com/watch?v=gC1x9cU0-tU
H2020 ; 694277 - TUNE - Testing the Untestable: Model Testing of Complex Software-Intensive Systems

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