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Revisiting Neuron Coverage and its Application to Test Generation

: Abrecht, Stephanie; Akila, Maram; Gannamaneni, Sujan Sai; Groh, Konrad; Heinzemann, Christian; Houben, Sebastian; Woehrle, Matthias


Casimiro, António:
Computer Safety, Reliability, and Security. Proceedings : SAFECOMP 2020 Workshops, DECSoS 2020, DepDevOps 2020, USDAI 2020, and WAISE 2020, Lisbon, Portugal, September 15, 2020, virtual conference
Cham: Springer Nature, 2020 (Lecture Notes in Computer Science 12235)
ISBN: 978-3-030-55582-5 (Print)
ISBN: 978-3-030-55583-2 (Online)
International Conference on Computer Safety, Reliability and Security (SafeComp) <39, 2020, Online>
International Workshop on Artificial Intelligence Safety Engineering (WAISE) <3, 2020, Online>
Bundesministerium fur Wirtschaft und Energie BMWi (Deutschland)
VDA Leitinitiative autonomes und vernetztes Fahren; 19A19005X
KI Absicherung - Safe AI for Automated Driving
Fraunhofer IAIS ()
Deep neural networks; Neurcon Coverage; Safe AI; Fashion MNIST

The use of neural networks in perception pipelines of autonomous systems such as autonomous driving is indispensable due to their outstanding performance but, at the same time, poses a challenge with respect to safety. An important question in this regard is how to substantiate test sufficiency for such a function. One approach from software testing literature is that of coverage metrics. Similar notions of coverage, called neuron coverage, have been proposed for deep neural networks and try to assess to what extent test input activates neurons in a network. Still, the correspondence between high neuron coverage and safety-related network qualities remains elusive. Potentially, a high coverage could imply sufficiency of test data. In this paper, we argue that the coverage metrics as discussed in the current literature do not satisfy these high expectations and present a line of experiments from the field of computer vision to prove this claim.