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Approximation-Refinement Testing of Compute-Intensive Cyber-Physical Models: An Approach Based on System Identification
Menghi, Claudio; Nejati, Shiva; Briand, Lionel et al.
2020In Proceedings of the 42nd International Conference on Software Engineering
Peer reviewed
 

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Keywords :
Cyber Physical Systems; Testing; Falsification
Abstract :
[en] Black-box testing has been extensively applied to test models of Cyber-Physical systems (CPS) since these models are not often amenable to static and symbolic testing and verification. Black-box testing, however, requires to execute the model under test for a large number of candidate test inputs. This poses a challenge for a large and practically-important category of CPS models, known as compute-intensive CPS (CI-CPS) models, where a single simulation may take hours to complete. We propose a novel approach, namely ARIsTEO, to enable effective and efficient testing of CI-CPS models. Our approach embeds black-box testing into an iterative approximation-refinement loop. At the start, some sampled inputs and outputs of the CI-CPS model under test are used to generate a surrogate model that is faster to execute and can be subjected to black-box testing. Any failure-revealing test identified for the surrogate model is checked on the original model. If spurious, the test results are used to refine the surrogate model to be tested again. Otherwise, the test reveals a valid failure. We evaluated ARIsTEO by comparing it with S-Taliro, an open-source and industry-strength tool for testing CPS models. Our results, obtained based on five publicly-available CPS models, show that, on average, ARIsTEO is able to find 24% more requirements violations than S-Taliro and is 31% faster than S-Taliro in finding those violations. We further assessed the effectiveness and efficiency of ARIsTEO on a large industrial case study from the satellite domain. In contrast to S-Taliro, ARIsTEO successfully tested two different versions of this model and could identify three requirements violations, requiring four hours, on average, for each violation.
Research center :
ULHPC - University of Luxembourg: High Performance Computing
Disciplines :
Computer science
Author, co-author :
Menghi, Claudio ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Nejati, Shiva ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Briand, Lionel ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Yago, Isasi Parache;  Luxspace Sàrl
External co-authors :
no
Language :
English
Title :
Approximation-Refinement Testing of Compute-Intensive Cyber-Physical Models: An Approach Based on System Identification
Publication date :
2020
Event name :
International Conference on Software Engineering
Event date :
from 23-05-2020 to 29-05-2020
Main work title :
Proceedings of the 42nd International Conference on Software Engineering
Peer reviewed :
Peer reviewed
Focus Area :
Security, Reliability and Trust
European Projects :
H2020 - 694277 - TUNE - Testing the Untestable: Model Testing of Complex Software-Intensive Systems
Funders :
CE - Commission Européenne [BE]
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since 14 January 2020

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