Importance-Driven Deep Learning System Testing

  title={Importance-Driven Deep Learning System Testing},
  author={Simos Gerasimou and Hasan Ferit Eniser and A. Sen and Alper Cakan},
  journal={2020 IEEE/ACM 42nd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)},
Deep Learning (DL) systems are key enablers for engineering intelligent applications. Nevertheless, using DL systems in safety- and security-critical applications requires to provide testing evidence for their dependable operation. We introduce DeepImportance, a systematic testing methodology accompanied by an Importance-Driven (IDC) test adequacy criterion for DL systems. Applying IDC enables to establish a layer-wise functional understanding of the importance of DL system components and use… Expand
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