Systems Analysis using Model Checking with Causality
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Date
2015
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Publisher
Université de M'sila
Abstract
Model checking is one of the most famous formal methods used for the verification of
finite-state systems. Given a system model and such specification which is a set of formal
proprieties, the model checker verifies whether or not the model meets the specification. In
case the specification is not satisfied, a counterexample is generated as an error trace.
Probabilistic model checking has appeared as an extension of model checking for analysing
systems that exhibit stochastic behaviour. Probabilistic model checking employs many
numerical algorithms to compute the probability of the satisfaction of given temporal property,
and thus it could determine whether a probabilistic property is satisfied or not given
such threshold. In case the probability threshold is violated, a counterexample is generated.
In this thesis, we show that the task of counterexamples generation in probabilistic model
checking has a quantitative aspect. As it is in conventional model checking, in probabilistic
model checking the generated counterexample should be small and indicative to be easy for
analysing. However, generating small and indicative counterexamples only is not enough
for understanding the error, especially that probabilistic counterexample consists of multiple
paths and it is probabilistic. Therefore, the analysis of probabilistic counterexamples is
inevitable to better understand the error.
This thesis addresses for the first time the complementary task of counterexample generation
in probabilistic model checking, which is the counterexample analysis. We propose
many aided-diagnostic methods for probabilistic counterexamples based on notions related
to causality theory. These methods guide the user to the most relevant parts of the model
that led to the error. We evaluate our methods using many case studies.
In probabilistic model checking, several case studies in several domains have been addressed
. In recent years there has been also a great attend to use probabilistic model checking
to analyse the dynamic and the performance of biological systems. With the growing
importance of probabilistic model checking as a formal framework for the verification and
quantitative analysis of probabilistic systems, we investigate this importance by showing its
applicability on two different domains, medical treatment analysis and probabilistic Complex
Event Processing (CEP).
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Keywords
Systems Analysis , Model Checking , Causality