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Formal Methods: Mechanically Checking the Semantics of Hybrid Event-B

  • Full or part time
  • Application Deadline
    Applications accepted all year round
  • Competition Funded PhD Project (Students Worldwide)
    Competition Funded PhD Project (Students Worldwide)

Project Description

These days, the world is increasingly being populated with systems that interact directly with the physical world. Of course such systems have existed for a long time in niche safety-critical areas, eg. avionics, train control, nuclear plant shutdown, etc. Now the physical world is described using continuous mathematics, while the systems in contact with it are digital, being described using discrete mathematics. These two spheres don’t mix terribly well, and the interface has typically been handled by ad hoc means. The hybrid and cyberphysical systems that are increasingly seen today are hard to analyse, and one way of approaching their design is top-down, where their properties are controlled from the outset and built up incrementally. The Event-B formalism is a discrete top-down formal development methodology which has become popular in the discrete domain, and Hybrid Event-B has been designed to extend it into the continuous domain. The aim of this project is to build a model of the semantics of Hybrid Event-B in a meta theorem prover like Isabelle/HoL or CoQ, and to demonstrate its soundness and the soundness of the Hybrid Event-B Proof Obligations.

Funding Notes

This research project is one of a number of projects at this institution. It is in competition for funding with one or more of these projects. Usually the project which receives the best applicant will be awarded the funding. Applications for this project are welcome from suitably qualified candidates worldwide. Funding may only be available to a limited set of nationalities and you should read the full department and project details for further information.

How good is research at University of Manchester in Computer Science and Informatics?

FTE Category A staff submitted: 44.86

Research output data provided by the Research Excellence Framework (REF)

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