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Thesis

Learning symbolic abstractions from system execution traces

Abstract:

This dissertation shows that symbolic abstractions for a system can be inferred from a set of system execution traces using a combination of Boolean satisfiability and program synthesis. In addition, the degree of completeness of an inferred abstraction can be evaluated by employing equivalence checking using simulation relations, that can further be used to iteratively infer an overapproximating system abstraction with provable completeness guarantees.

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Division:
MPLS
Department:
Computer Science
Role:
Author

Contributors

Institution:
University of Oxford
Role:
Supervisor
ORCID:
0000-0002-2462-2782
Institution:
University of Oxford
Role:
Supervisor
ORCID:
0000-0002-6681-5283
More from this funder
Name:
Semiconductor Research Corporation
Funder identifier:
http://dx.doi.org/10.13039/100000028
Funding agency for:
Yogananda Jeppu, N
More from this funder
Name:
Jason Hu Scholarship
Funding agency for:
Yogananda Jeppu, N
Type of award:
DPhil
Level of award:
Doctoral
Awarding institution:
University of Oxford
Language:
English
Keywords:
Subjects:
Deposit date:
2023-04-27

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