2026-04-07T22:03:57Zhttps://ebiltegia.mondragon.edu/oai/request

oai:ebiltegia.mondragon.edu:20.500.11984/139832026-01-29T08:38:06Zcom_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 safety and mission-critical software systems. To ensure a high dependability of DL systems, robust verification methods are required, for which automation is highly beneficial (e.g., more test cases can be executed). Metamorphic Testing (MT) is a technique that has shown to alleviate the test oracle problem when testing DL systems, and therefore, increasing test automation. However, a drawback of this technique lies into the need of multiple test executions to obtain the test verdict (named as the source and the follow-up test cases), requiring additional testing cost. In this paper we propose an approach based on multi-objective search to select follow-up test cases. Our approach makes use of source test cases to measure the uncertainty provoked by such test inputs in the DL model, and based on that, select failure-revealing follow-up test cases. We integrate our approach with the NSGA-II algorithm. An empirical evaluation on three DL models tackling the image classification problem, along with five different metamorphic relations demonstrates that our approach outperformed the baseline algorithm between 17.09 to 59.20% on average when considering the revisited Hypervolume quality indicator. 978-1-4503-9237-2 https://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=167746 https://hdl.handle.net/20.500.11984/13983 Multi-Objective Metamorphic Follow-up Test Case Selection for Deep Learning Systems