Abstract
| Original language | English |
|---|---|
| Number of pages | 15 |
| Journal | IEEE Transactions on Software Engineering |
| DOIs | |
| Publication status | Published - 14 Dec 2018 |
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Automatically ‘Verifying’ Discrete-Time Complex Systems through Learning, Abstraction and Refinement. / Wang, Jingyi; Sun, Jun; Qin, Shengchao; Jegourel, Cyrille.
In: IEEE Transactions on Software Engineering, 14.12.2018.Research output: Contribution to journal › Article › Research › peer-review
TY - JOUR
T1 - Automatically ‘Verifying’ Discrete-Time Complex Systems through Learning, Abstraction and Refinement
AU - Wang, Jingyi
AU - Sun, Jun
AU - Qin, Shengchao
AU - Jegourel, Cyrille
PY - 2018/12/14
Y1 - 2018/12/14
N2 - Precisely modeling complex systems like cyber-physical systems is challenging, which often render model-based system verification techniques like model checking infeasible. To overcome this challenge, we propose a method called LAR to automatically ‘verify’ such complex systems through a combination of learning, abstraction and refinement from a set of system log traces. We assume that log traces and sampling frequency are adequate to capture ‘enough’ behaviour of the system. Given a safety property and the concrete system log traces as input, LAR automatically learns and refines system models, and produces two kinds of outputs. One is a counterexample with a bounded probability of being spurious. The other is a probabilistic model based on which the given property is ‘verified’. The model can be viewed as a proof obligation, i.e., the property is verified if the model is correct. It can also be used for subsequent system analysis activities like runtime monitoring or model-based testing. Our method has been implemented as a self-contained software toolkit. The evaluation on multiple benchmark systems as well as a real-world water treatment system shows promising results.
AB - Precisely modeling complex systems like cyber-physical systems is challenging, which often render model-based system verification techniques like model checking infeasible. To overcome this challenge, we propose a method called LAR to automatically ‘verify’ such complex systems through a combination of learning, abstraction and refinement from a set of system log traces. We assume that log traces and sampling frequency are adequate to capture ‘enough’ behaviour of the system. Given a safety property and the concrete system log traces as input, LAR automatically learns and refines system models, and produces two kinds of outputs. One is a counterexample with a bounded probability of being spurious. The other is a probabilistic model based on which the given property is ‘verified’. The model can be viewed as a proof obligation, i.e., the property is verified if the model is correct. It can also be used for subsequent system analysis activities like runtime monitoring or model-based testing. Our method has been implemented as a self-contained software toolkit. The evaluation on multiple benchmark systems as well as a real-world water treatment system shows promising results.
U2 - 10.1109/TSE.2018.2886898
DO - 10.1109/TSE.2018.2886898
M3 - Article
JO - IEEE Transactions on Software Engineering
JF - IEEE Transactions on Software Engineering
SN - 0098-5589
ER -