Systems Analysis using Model Checking with Causality
| dc.contributor.author | DEBBI, Hichem | |
| dc.date.accessioned | 2018-02-25T12:52:06Z | |
| dc.date.available | 2018-02-25T12:52:06Z | |
| dc.date.issued | 2015 | |
| dc.description.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). | en_US |
| dc.identifier.uri | https://dspace.univ-msila.dz/handle/123456789/3125 | |
| dc.language.iso | en | en_US |
| dc.publisher | Université de M'sila | en_US |
| dc.subject | Systems Analysis , Model Checking , Causality | en_US |
| dc.title | Systems Analysis using Model Checking with Causality | en_US |
| dc.type | Thesis | en_US |