Advanced search options

Advanced Search Options 🞨

Browse by author name (“Author name starts with…”).

Find ETDs with:

in
/  
in
/  
in
/  
in

Written in Published in Earliest date Latest date

Sorted by

Results per page:

Sorted by: relevance · author · university · dateNew search

You searched for +publisher:"York University" +contributor:("Breugel, Franck van"). Showing records 1 – 2 of 2 total matches.

Search Limiters

Last 2 Years | English Only

No search limiters apply to these results.

▼ Search Limiters

1. Shafiei, Nastaran. Model Checking of Distributed Multi-Threaded Java Applications.

Degree: PhD, Computer Science, 2015, York University

In this dissertation, we focus on the verification of distributed Java applications composed of communicating multithreaded processes. We choose model checking as the verification technique. We propose an instance of the so-called centralization approach which allows for model checking multiple communicating processes. The main challenge of applying centralization is keeping data separated between different processes. In our approach, this issue is addressed through a new class-loading model. As one of our contributions, we implement our approach within an existing model checker, Java PathFinder (JPF). To account for interactions between processes, our approach provides the model checker with a model of interprocess communication. Moreover, our model allows for systematically exploring potential exceptional control flows caused by network failures. We also apply a partial order reduction (POR) algorithm to reduce the state space of distributed applications, and we prove that our POR algorithm preserves deadlocks. Furthermore, we propose an automatic approach to capture interactions between the system being verified and external resources, such as cloud computing services. The dissertation also discusses how our approach is superior to existing approaches. Our approach exhibits better performance which is mainly due to the POR technique. Furthermore, our approach allows for verifying a considerably larger class of applications without the need for any manual modeling, and it has been successfully used to detect bugs that cannot be found using previous work. Advisors/Committee Members: Breugel, Franck van (advisor).

Subjects/Keywords: Computer science; Computer engineering; Partial order reduction; Software verification; Model checking; Distributed application; Concurrency; Java; Java PathFinder; Class-loading model; Native calls; State space reduction

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Shafiei, N. (2015). Model Checking of Distributed Multi-Threaded Java Applications. (Doctoral Dissertation). York University. Retrieved from http://hdl.handle.net/10315/28190

Chicago Manual of Style (16th Edition):

Shafiei, Nastaran. “Model Checking of Distributed Multi-Threaded Java Applications.” 2015. Doctoral Dissertation, York University. Accessed September 23, 2019. http://hdl.handle.net/10315/28190.

MLA Handbook (7th Edition):

Shafiei, Nastaran. “Model Checking of Distributed Multi-Threaded Java Applications.” 2015. Web. 23 Sep 2019.

Vancouver:

Shafiei N. Model Checking of Distributed Multi-Threaded Java Applications. [Internet] [Doctoral dissertation]. York University; 2015. [cited 2019 Sep 23]. Available from: http://hdl.handle.net/10315/28190.

Council of Science Editors:

Shafiei N. Model Checking of Distributed Multi-Threaded Java Applications. [Doctoral Dissertation]. York University; 2015. Available from: http://hdl.handle.net/10315/28190

2. Tang, Qiyi. Computing Probabilistic Bisimilarity Distances.

Degree: PhD, Computer Science, 2018, York University

Behavioural equivalences like probabilistic bisimilarity rely on the transition probabilities and, as a result, are sensitive to minuscule changes of those probabilities. Such behavioural equivalences are not robust, as first observed by Giacalone, Jou and Smolka. Probabilistic bisimilarity distances, a robust quantitative generalization of probabilistic bisimilarity, capture the similarity of the behaviour of states of a probabilistic model. The smaller the distance, the more alike the states behave. In particular, states are probabilistic bisimilar if and only if the distance between them is zero. In this dissertation, we focus on algorithms to compute probabilistic bisimilarity distances for two probabilistic models: labelled Markov chains and probabilistic automata. In the late nineties, Desharnais, Gupta, Jagadeesan and Panangaden defined probabilistic bisimilarity distances on the states of a labelled Markov chain. This provided a quantitative generalization of probabilistic bisimilarity, which was introduced by Larsen and Skou a decade earlier. Several algorithms to approximate and compute these probabilistic bisimilarity distances have been put forward. In this dissertation, we correct and generalize some of these policy iteration algorithms. Moreover, we develop several new algorithms which have better performance in practice and can handle much larger systems. Similarly, Deng, Chothia, Palamidessi and Pang presented probabilistic bisimilarity distances on the states of a probabilistic automaton. This provided a robust quantitative generalization of probabilistic bisimilarity introduced by Segala and Lynch. Although the complexity of computing probabilistic bisimilarity distances for probabilistic automata has already been studied and shown to be in NP coNP and PPAD, we are not aware of any practical algorithms to compute those distances. In this dissertation, we provide several key results that may prove to be useful for the development of algorithms to compute probabilistic bisimilarity distances for probabilistic automata. In particular, we present a polynomial time algorithm that decides distance one. Furthermore, we give an alternative characterization of the probabilistic bisimilarity distances as a basis for a policy iteration algorithm. Advisors/Committee Members: Breugel, Franck van (advisor).

Subjects/Keywords: Computer science; Probabilistic bisimilarity distances; Bisimulation metric; Probabilistic models; Model checking; Concurrency; Labelled Markov chains; Algorithms; Probabilistic automata

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Tang, Q. (2018). Computing Probabilistic Bisimilarity Distances. (Doctoral Dissertation). York University. Retrieved from http://hdl.handle.net/10315/35586

Chicago Manual of Style (16th Edition):

Tang, Qiyi. “Computing Probabilistic Bisimilarity Distances.” 2018. Doctoral Dissertation, York University. Accessed September 23, 2019. http://hdl.handle.net/10315/35586.

MLA Handbook (7th Edition):

Tang, Qiyi. “Computing Probabilistic Bisimilarity Distances.” 2018. Web. 23 Sep 2019.

Vancouver:

Tang Q. Computing Probabilistic Bisimilarity Distances. [Internet] [Doctoral dissertation]. York University; 2018. [cited 2019 Sep 23]. Available from: http://hdl.handle.net/10315/35586.

Council of Science Editors:

Tang Q. Computing Probabilistic Bisimilarity Distances. [Doctoral Dissertation]. York University; 2018. Available from: http://hdl.handle.net/10315/35586

.