Reference : Automatic Test Suite Generation for Key-points Detection DNNs Using Many-Objective Search
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
Security, Reliability and Trust
http://hdl.handle.net/10993/46959
Automatic Test Suite Generation for Key-points Detection DNNs Using Many-Objective Search
English
Ul Haq, Fitash mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SVV >]
Shin, Donghwan mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SVV >]
Briand, Lionel mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SVV >]
Stifter, Thomas [IEE S.A.]
Wang, Jun [Post Luxembourg]
In press
2021 ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA)
Yes
International
INTERNATIONAL SYMPOSIUM ON SOFTWARE TESTING AND ANALYSIS
from 11-07-2021 to 17-07-2021
Virtual
Virtual
[en] Key-point detection ; deep neural network ; software testing ; many-objective search algorithm
[en] Automatically detecting the positions of key-points (e.g., facial key-points or finger key-points) in an image is an essential problem in many applications, such as driver's gaze detection and drowsiness detection in automated driving systems. With the recent advances of Deep Neural Networks (DNNs), Key-Points detection DNNs (KP-DNNs) have been increasingly employed for that purpose. Nevertheless, KP-DNN testing and validation have remained a challenging problem because KP-DNNs predict many independent key-points at the same time---where each individual key-point may be critical in the targeted application---and images can vary a great deal according to many factors.

In this paper, we present an approach to automatically generate test data for KP-DNNs using many-objective search. In our experiments, focused on facial key-points detection DNNs developed for an industrial automotive application, we show that our approach can generate test suites to severely mispredict, on average, more than 93% of all key-points. In comparison, random search-based test data generation can only severely mispredict 41% of them. Many of these mispredictions, however, are not avoidable and should not therefore be considered failures. We also empirically compare state-of-the-art, many-objective search algorithms and their variants, tailored for test suite generation. Furthermore, we investigate and demonstrate how to learn specific conditions, based on image characteristics (e.g., head posture and skin color), that lead to severe mispredictions. Such conditions serve as a basis for risk analysis or DNN retraining.
http://hdl.handle.net/10993/46959
H2020 ; 694277 - TUNE - Testing the Untestable: Model Testing of Complex Software-Intensive Systems
FnR ; FNR14711346 > Fabrizio Pastore > FUNTASY > Functional Safety For Autonomous Systems > 01/08/2020 > 31/07/2023 > 2020

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