2026-04-08T19:30:38Zhttps://ebiltegia.mondragon.edu/oai/request

oai:ebiltegia.mondragon.edu:20.500.11984/59062024-03-04T09:38:01Zcom_20.500.11984_1143col_20.500.11984_1148

00925njm 22002777a 4500 dc Arrieta, Aitor author 2022 Deep Learning (DL) components are increasing their presence in mission and safety-critical systems, such as autonomous vehicles. The verification process of such systems needs to be rigorous, for which automated solutions are paramount. To allow test automation, test oracles are necessary. In the context of DL systems, meta-morphic test oracles have found to be effective. However, such oracles require the execution of multiple tests, which makes testing more expensive. Metamorphic relation composition can reduce the cost of metamorphic testing. However, its effectiveness has found mixed answers. This paper reports the preliminary results of our study on measuring the cost-effectiveness of composite metamor-phic relations for testing DL systems. To this end, we empirically evaluate the cost-effectiveness of composite metamorphic relations within a DL model for object classification. Our results suggest that composite metamorphic relations reduce the failure revealing capability when compared to their component metamorphic relations. 978-1-4503-9307-2 https://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=167748 https://hdl.handle.net/20.500.11984/5906 Deep learning Learning systems Costs Automation Conferences Autonomous vehicles testing On the Cost-Effectiveness of Composite Metamorphic Relations for Testing Deep Learning Systems