Importance-driven deep learning system testing
Simos Gerasimou, Hasan Ferit Enişer, Alper Şen, Alper Çakan
Abstract
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 this information to assess the semantic diversity of a test set. Our empirical evaluation on several DL systems and across multiple DL datasets demonstrates the usefulness and effectiveness of DeepImportance.