TY - GEN
T1 - Falsification of cyber-physical systems with robustness uncertainty quantification through stochastic optimization with adaptive restart
AU - Mathesen, Logan
AU - Yaghoubi, Shakiba
AU - Pedrielli, Giulia
AU - Fainekos, Georgios
N1 - Funding Information:
This research was partially funded by the awards NSF 1350420, NSF 1829238, NSF 1361926, NSF 1841051, and the NSF I/UCRC Center for Embedded Systems
Funding Information:
1L. Mathesen, S. Yaghoubi, G. Pedrielli, and G. Fainekos are with the School of Computing Informatics and Decision Systems Engineering, Arizona State University, Brickyard Engineering, 699 S Mill Ave, Tempe, Az, USA. lmathese@asu.edu, syaghoub@asu.edu, giulia.pedrielli@asu.edu, fainekos@asu.edu This research was partially funded by the awards NSF 1350420, NSF 1829238, NSF 1361926, NSF 1841051, and the NSF I/UCRC Center for Embedded Systems.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/8
Y1 - 2019/8
N2 - This work is in the field of requirements driven search-based test case generation methods for Cyber-Physical Systems (CPS). The basic characteristic of search-based testing methods is that the search process is guided by high level requirements captured in formal logic and, in particular, Signal Temporal Logic (STL). Given a system trajectory, STL specifications can be equipped with quantitative semantics which evaluate the closeness of the given trajectory from violating the requirement. Hence, by searching for trajectories of decreasing value with respect to the specification, a test generation method can be formulated which searches for system behaviors with a closeness to violation value of less than 0. These system behaviors, i.e., trajectories that violate the requirements and yield STL closeness value less than 0, are referred to as falsiping behaviors. In addition, signed distance can be utilized when searching for trajectories that maximally violate the specification (negative specification valuations). In this work, we propose the use of a stochastic search method that mixes global and local search for system test case generation. The implemented search method models input-output relationships between test cases and the observed STL closeness values of the yielded system trajectories, adaptively linking input-out of both global and local regional modeling. The method shows improved finite time performance, i.e., quick identification of falsification behaviors, over current search-based test case generation methods. Further, given no falsifying behaviors are found in finite time our method is capable of quantifying the certainty that no falsifying behaviors exist.
AB - This work is in the field of requirements driven search-based test case generation methods for Cyber-Physical Systems (CPS). The basic characteristic of search-based testing methods is that the search process is guided by high level requirements captured in formal logic and, in particular, Signal Temporal Logic (STL). Given a system trajectory, STL specifications can be equipped with quantitative semantics which evaluate the closeness of the given trajectory from violating the requirement. Hence, by searching for trajectories of decreasing value with respect to the specification, a test generation method can be formulated which searches for system behaviors with a closeness to violation value of less than 0. These system behaviors, i.e., trajectories that violate the requirements and yield STL closeness value less than 0, are referred to as falsiping behaviors. In addition, signed distance can be utilized when searching for trajectories that maximally violate the specification (negative specification valuations). In this work, we propose the use of a stochastic search method that mixes global and local search for system test case generation. The implemented search method models input-output relationships between test cases and the observed STL closeness values of the yielded system trajectories, adaptively linking input-out of both global and local regional modeling. The method shows improved finite time performance, i.e., quick identification of falsification behaviors, over current search-based test case generation methods. Further, given no falsifying behaviors are found in finite time our method is capable of quantifying the certainty that no falsifying behaviors exist.
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UR - http://www.scopus.com/inward/record.url?scp=85072979808&partnerID=8YFLogxK
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U2 - 10.1109/COASE.2019.8843005
DO - 10.1109/COASE.2019.8843005
M3 - Conference contribution
AN - SCOPUS:85072979808
T3 - IEEE International Conference on Automation Science and Engineering
SP - 991
EP - 997
BT - 2019 IEEE 15th International Conference on Automation Science and Engineering, CASE 2019
PB - IEEE Computer Society
T2 - 15th IEEE International Conference on Automation Science and Engineering, CASE 2019
Y2 - 22 August 2019 through 26 August 2019
ER -