@inproceedings{fe69fa7866664613bcd7bc1715e16a97,
title = "DeepHyperion: Exploring the Feature Space of Deep Learning-based Systems through Illumination Search",
abstract = "In this extended abstract, we summarize our contributions to automated testing of Deep Learning-based systems published at the ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) in 2021 [Zo21a] and just accepted by the ACM Transactions on Software Engineering and Methodology (TOSEM) in 2022 [Zo22]. Deep Learning-based systems (DL Systems) find applications in safety-critical application domains and thus must be thoroughly tested. Existing DL system testing approaches can generate complex and fault-finding inputs but do not characterize them in a way that enables human interpretation and do not always consider test diversity. Our work addresses these challenges and can find effective and diverse test cases.",
keywords = "deep learning, search-based software engineering, self-driving cars, Software testing",
author = "Alessio Gambi and Paolo Tonella and Vincenzo Riccio and Tahereh Zohdinasab",
note = "Publisher Copyright: {\textcopyright} 2023 Gesellschaft fur Informatik (GI). All rights reserved.",
year = "2023",
month = feb,
day = "24",
language = "English",
volume = "P-332",
series = "Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI)",
publisher = "Gesellschaft f{\"u}r Informatik e.V.",
pages = "131--132",
editor = "Gregor Engels and Regina Hebig and Matthias Tichy",
booktitle = "Software Engineering 2023 - Fachtagung des GI-Fachbereichs Softwaretechnik",
}