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Comparing Offline and Online Testing of Deep Neural Networks: An Autonomous Car Case Study
There is a growing body of research on developing testing techniques for...
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Misbehaviour Prediction for Autonomous Driving Systems
Deep Neural Networks (DNNs) are the core component of modern autonomous ...
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Input Prioritization for Testing Neural Networks
Deep neural networks (DNNs) are increasingly being adopted for sensing a...
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Computing the Testing Error without a Testing Set
Deep Neural Networks (DNNs) have revolutionized computer vision. We now ...
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Click-Through Rate Prediction with the User Memory Network
Click-through rate (CTR) prediction is a critical task in online adverti...
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Significant acceleration of development by automating quality assurance of a medical particle accelerator safety system using a formal language driven test stand
At the Centre for Proton Therapy at the Paul Scherrer Institute cancer p...
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SINVAD: Search-based Image Space Navigation for DNN Image Classifier Test Input Generation
The testing of Deep Neural Networks (DNNs) has become increasingly impor...
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Can Offline Testing of Deep Neural Networks Replace Their Online Testing?
We distinguish two general modes of testing for Deep Neural Networks (DNNs): Offline testing where DNNs are tested as individual units based on test datasets obtained independently from the DNNs under test, and online testing where DNNs are embedded into a specific application environment and tested in a closed-loop mode in interaction with the application environment. Typically, DNNs are subjected to both types of testing during their development life cycle where offline testing is applied immediately after DNN training and online testing follows after offline testing and once a DNN is deployed within a specific application environment. In this paper, we study the relationship between offline and online testing. Our goal is to determine how offline testing and online testing differ or complement one another and if we can use offline testing results to run fewer tests during online testing to reduce the testing cost. Though these questions are generally relevant to all autonomous systems, we study them in the context of automated driving systems where, as study subjects, we use DNNs automating end-to-end controls of steering functions of self-driving vehicles. Our results show that offline testing is more optimistic than online testing as many safety violations identified by online testing could not be identified by offline testing, while large prediction errors generated by offline testing always led to severe safety violations detectable by online testing. Further, we cannot use offline testing results to run fewer tests during online testing in practice since we are not able to identify specific situations where offline testing could be as accurate as online testing in identifying safety requirement violations.
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