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Mutation testing for simulated autonomous vehicles

  • Full or part time
  • Application Deadline
    Applications accepted all year round
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

Research areas: Automated and Model-Driven Software Engineering; Autonomous and self-adaptive systems; Cyber Physical Systems;
Safety of autonomous and self-adaptive systems; Software engineering; Software testing

Mutation testing is an effective way to generate thorough tests for critical systems [1] and to evaluate testing methods [2]. It has been widely applied to many kinds of software, but not specifically to autonomous vehicles.

There is thus potential for a PhD project to produce a mutation testing approach to generate seeded faults in simulated autonomous vehicles:

• Identify a class of algorithms to target (such as aircraft collision avoidance algorithms)
• Construct an abstract model of a vehicle implementing such an algorithm (perhaps as a domain-specific language)
• Produce a set of mutation operators that apply to your model
• Evaluate your operators by assessing whether the faults they produce can defeat simple approaches to autonomous vehicle testing (e.g. randomly generating test situations)


[1] Y. Jia and M. Harman, ‘An Analysis and Survey of the Development of Mutation Testing’, IEEE Transactions on Software Engineering, vol. 37, no. 5, pp. 649–678, Sep. 2011.

[2] J. H. Andrews, L. C. Briand, and Y. Labiche, ‘Is Mutation an Appropriate Tool for Testing Experiments?’, in Proceedings of the 27th International Conference on Software Engineering, New York, NY, USA, 2005, pp. 402–411.

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

FTE Category A staff submitted: 34.80

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

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