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PhD studentship on Probabilistic Property-Based Testing

Project: Probabilistic Property-Based Testing
Supervisors:Vaishak Belle
James Cheney

We seek strong candidates for a 3-year PhD research studentship at the University of Edinburgh. The studentship will be supervised by Dr. Vaishak Belle and Dr. James Cheney and is supported by Huawei.

Project description

We are now accepting applications for 3-year PhD studentship on a project called "Probabilistic property-based testing" in the School of Informatics, University of Edinburgh.

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.

Related publications

The following papers and other sources are potential starting points for the project:

  • C. Amaral, M. Florido, and V. Santos Costa. PrologCheck: Property-based testing in Prolog. In M. Codish and E. Sumii, editors, Functional and Logic Programming, volume 8475 of LNCS, pages 1-17. Springer, 2014.
  • J. Cheney, A. Momigliano: αCheck: A mechanized metatheory model checker. TPLP 17(3): 311-352 (2017)
  • J. Cheney, A. Momigliano, M. Pessina: Advances in Property-Based Testing for alphaProlog. CoRR abs/1604.08345 (2016)
  • K. Claessen and J. Hughes. QuickCheck: a lightweight tool for random testing of Haskell programs. In ICFP, 2000.
  • L. De Raedt, A. Dries, I. Thon, G. Van den Broeck, and M. Verbeke. Inducing probabilistic relational rules from probabilistic examples. In IJCAI 2015, pages 1835-1843, 2015.
  • B. Fetscher, K. Claessen, M. H. Palka, J. Hughes, and R. B. Findler. Making random judgments: Automatically generating well-typed terms from the definition of a type-system. In ESOP, 2015.
  • D. Fierens, G. Van den Broeck, J. Renkens, D. S. Shterionov, B. Gutmann, I. Thon, G. Janssens, and L. De Raedt. Inference and learning in probabilistic logic programs using weighted Boolean formulas. TPLP, 15(3):358-401, 2015.
  • F. Hebert. Property-based testing with PropEr, Erlang, and Elixir. Pragmatic Programmers, 2018.
  • N. D. Goodman, J. B. Tenenbaum, and The ProbMods Contributors (2016). Probabilistic Models of Cognition (2nd ed.). Retrieved 2019-1-6 from https://probmods.org/
  • J. Midtgaard, M. N. Justesen, P. Kasting, F. Nielson, and H. R. Nielson. Effect-driven quickchecking of compilers. PACMPL, 1(ICFP):15:1-15:23, 2017.
  • Agustín Mista, Alejandro Russo, and John Hughes. 2018. Branching processes for QuickCheck generators. In Proceedings of the 11th ACM SIGPLAN International Symposium on Haskell (Haskell 2018). ACM, New York, NY, USA, 1-13.
  • R. Perera, U. A. Acar, J. Cheney, and P. B. Levy. Functional programs that explain their work. In ICFP, 2012.
  • S. Speichert and V. Belle. Learning probabilistic logic programs in continuous domains. CoRR, abs/1807.05527, 2018.

Prospective applicants are also encouraged to experiment with QuickCheck-style libraries in Haskell, Scala or other languages, or with probabilistic programming languages such as WebPPL or ProbLog.

Background required

The PhD student supported by this funding will carry out fundamental research on probabilistic programming for software testing. The ideal candidate would have a strong background in functional or logic programming (e.g. Haskell, OCaml, Erlang, Prolog), or a strong background in machine learning. Candidates already familiar with probabilistic programming or symbolic machine learning (e.g. relational learning, probabilistic logic programming) are especially welcome.

About the position

The studentship is tenable for 3 years, and covers full tuition fees for a student of any nationality, as well as a stipend of £14,777 per year (tax free and increasing with inflation), supported by Huawei. The School is also partnered with data science and AI centres of excellence such as The Alan Turing Institute in London and the Bayes Centre in Edinburgh, and there will be ample opportunities to engage with these institutes, via workshops and other schemes.

Application information

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.

To apply, please submit an application to the 3-year CISA PhD programme:

3-year CISA PhD application for September 2019 entry

Further instructions and information about PhD study at CISA and the PhD application process is available here:

In your application, please:

  • list Vaishak Belle and/or James Cheney in the "Proposed Supervisor" field;
  • list Probabilistic Property-Based Testing in the "Research Topic" field; and
  • the research proposal should address a topic relevant to the project you are applying for, it should not simply be a restatement of this advertisement.

For more information please contact Vaishak Belle (vaishak@ed.ac.uk) and/or James Cheney (jcheney@inf.ed.ac.uk).

Environment

The University of Edinburgh School of Informatics brings together world-class research groups in theoretical computer science, artificial intelligence and cognitive science. The School led the UK 2014 REF rankings in volume of internationally recognized or internationally excellent research. In 2013, the School of Informatics received an Athena Swan Silver Award, in recognition of its commitment to advancing the careers of women in science, technology, engineering, mathematics and medicine (STEMM) employment in higher education and research. Overall the University of Edinburgh has achieved a Silver Award.

For more information about study in Edinburgh and the School of Informatics, see these pages:

Last modified: Tue Sep 22 12:38:06 BST 2020 Accessibility statement