Combining Model Learning and Model Checking to Analyze Java Libraries

2020 ◽  
pp. 259-278
Author(s):  
Shahbaz Ali ◽  
Hailong Sun ◽  
Yongwang Zhao
Keyword(s):  
Model Checking ◽  
Model Learning

scholarly journals Model learning and model checking of SSH implementations

2017 ◽  
Author(s):  
Paul Fiterău-Broştean ◽  
Toon Lenaerts ◽  
Erik Poll ◽  
Joeri de Ruiter ◽  
Frits Vaandrager ◽  
...  
Keyword(s):  
Model Checking ◽  
Model Learning

scholarly journals Model Learning and Model Checking of IPSec Implementations for Internet of Things

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 171322-171332 ◽  
Author(s):  
Jiaxing Guo ◽  
Chunxiang Gu ◽  
Xi Chen ◽  
Fushan Wei
Keyword(s):  
Internet Of Things ◽  
Model Checking ◽  
Model Learning

scholarly journals Reduction Techniques for Model Checking and Learning in MDPs

2017 ◽  
Author(s):  
Suda Bharadwaj ◽  
Stephane Le Roux ◽  
Guillermo Perez ◽  
Ufuk Topcu
Keyword(s):  
Model Checking ◽  
Transition Probabilities ◽  
Initial Distribution ◽  
Decision Processes ◽  
Probabilistic Model Checking ◽  
Model Learning ◽  
Reduction Techniques ◽  
Learning Techniques ◽  
Markov Decision ◽  
Target Set

Omega-regular objectives in Markov decision processes (MDPs) reduce to reachability: find a policy which maximizes the probability of reaching a target set of states. Given an MDP, an initial distribution, and a target set of states, such a policy can be computed by most probabilistic model checking tools. If the MDP is only partially specified, i.e., some prob- abilities are unknown, then model-learning techniques can be used to statistically approximate the probabilities and enable the computation of the de- sired policy. For fully specified MDPs, reducing the size of the MDP translates into faster model checking; for partially specified MDPs, into faster learning. We provide reduction techniques that al- low us to remove irrelevant transition probabilities: transition probabilities (known, or to be learned) that do not influence the maximal reachability probability. Among other applications, these reductions can be seen as a pre-processing of MDPs before model checking or as a way to reduce the number of experiments required to obtain a good approximation of an unknown MDP.

scholarly journals Combining Model Learning and Model Checking to Analyze TCP Implementations

2016 ◽  
pp. 454-471 ◽  
Author(s):  
Paul Fiterău-Broştean ◽  
Ramon Janssen ◽  
Frits Vaandrager
Keyword(s):  
Model Checking ◽  
Model Learning

Model Checking Control Software for Nonlinear Dynamic Systems

2008 ◽  
Author(s):  
James Kapinski ◽  
Alexandre Donze ◽  
Flavio Lerda ◽  
Hitashyam Maka ◽  
Edmund Clarke ◽  
...  
Keyword(s):  
Model Checking ◽  
Dynamic Systems ◽  
Nonlinear Dynamic ◽  
Nonlinear Dynamic Systems ◽  
Control Software
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