2026-04-21T00:23:14Zhttps://ebiltegia.mondragon.edu/oai/request

oai:ebiltegia.mondragon.edu:20.500.11984/139482026-01-29T08:37:40Zcom_20.500.11984_1143col_20.500.11984_1148

00925njm 22002777a 4500 dc Arrieta, Aitor author Valle Entrena, Pablo author Iriarte, Asier author Illarramendi, Miren author 2023 Cyber-Physical Systems (CPSs) combine digital cyber technologies with physical processes. As in any other software system, in the case of CPSs, the use of Artificial Intelligence (AI) techniques in general, and Deep Neural Networks (DNNs) in particular, is contantly increasing. While recent studies have considerably advanced the field of testing AI-enabled systems, it has not yet been investigated how different Deep Learning (DL) bugs affect AI-enabled CPSs in operation. This work-in-progress paper presents a preliminary evaluation on how such bugs can affect CPSs in operation by using a mobile robot as a case study system. For that, we generated DL mutants by using operators proposed by Humbatova et al., which are operators based on real-world DL faults. Our preliminary investigation suggests that such bugs are more difficult to detect when they are deployed in operation rather than when testing their DNN in an off-line setup, which contrast with related studies. 978-1-6654-5223-6 https://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=174051 https://hdl.handle.net/20.500.11984/13948 Deep learning Artificial Neural Networks Cyber Physical Systems ODS 9 Industria, innovación e infraestructura How Do Deep Learning Faults Affect AI-Enabled Cyber-Physical Systems in Operation? A Preliminary Study Based on DeepCrime Mutation Operators