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PhD Studentship in probabilistic property-based testing

Project Description

The aim of the project is to explore the hypothesis that property-based testing (e.g. QuickCheck) is a form of probabilistic programming. Property-based testing is a widely used and powerful form of lightweight randomized testing, but it has been developed largely independently of increasingly sophisticated probabilistic programming languages and inference algorithms. This project will study the consequences of adopting the perspective that property-based testing is a form of probabilistic programming, and investigate subproblems such as inducing good properties from programs or test data; testing complex programs using advanced sampling techniques that provide error bounds; and synthesizing suitable data generators or automatically providing concise explanations why a property fails to hold.

Possible application areas include randomized testing of programming language designs and type systems themselves (following e.g. PLT Redex), as well as traditional system specification and testing problems.

Applications from prospective students interested in starting a PhD in the next academic year should be submitted by March 18, 2019. Applications received by January 31, 2019 will receive full consideration; after that date applications will be considered until the position is filled. The anticipated start date is September 2019 but earlier start dates may be possible.

Further information about the project can be found here: or by contacting Dr. Vaishak Belle () or Dr. James Cheney ()

Funding Notes

The studentship is tenable for 3 years, and covers full tuition fees for a student of any nationality, as well as stipend of GBP 14,777 per year (tax free and increasing with inflation), supported by Huawei.

The School is also a partner in data science and AI centres of excellence such as The Alan Turing Institute in London and the Bayes center in Edinburgh, and there will be ample opportunities to engage with these institutes, via workshops and other schemes.

Related Subjects

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

FTE Category A staff submitted: 94.85

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

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