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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)

References

References
[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. https://doi.org/10.1109/TSE.2010.62

[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. https://doi.org/10.1145/1062455.1062530

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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