Abstract
Autonomous robots (AR) can get themselves into a wide range of situations, and they do not have a human to look after them in fine detail all the time. When we test autonomous robots, we must therefore care deeply about the range and diversity of the situations in which we
have simulated and tested them – we must make sure that the
situation coverage of our testing is adequate. Situation coverage measures can be implemented quantitatively, and so unlock a range of automated testing strategies. There are epistemic challenges to justifying the confidence we should attach to test results driven by situation coverage, but they are not fundamentally more difficult than those faced by other coverage criteria.
have simulated and tested them – we must make sure that the
situation coverage of our testing is adequate. Situation coverage measures can be implemented quantitatively, and so unlock a range of automated testing strategies. There are epistemic challenges to justifying the confidence we should attach to test results driven by situation coverage, but they are not fundamentally more difficult than those faced by other coverage criteria.
Original language | English |
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Publisher | Department of Computer Science, University of York |
Number of pages | 21 |
Volume | Report number YCS-2015-496 |
Publication status | Published - Feb 2015 |
Datasets
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Software from Testing Autonomous Vehicle Software using Situation Generation
Hawkins, H. R. (Creator) & Alexander, R. (Supervisor), University of York, 8 Feb 2017
DOI: 10.15124/1b0286c0-ceaf-4c70-8ec1-7895f090079e
Dataset
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Experiments from Testing Autonomous Vehicle Software using Situation Generation
Hawkins, H. R. (Creator) & Alexander, R. (Supervisor), University of York, 8 Feb 2017
DOI: 10.15124/1c787c9f-2860-46db-a323-242d8fe9aeb1
Dataset