ISSTA 2019
Mon 15 - Fri 19 July 2019 Beijing, China
Thu 18 Jul 2019 14:00 - 14:22 at Grand Ballroom - Testing and Machine Learning Chair(s): Hongyu Zhang

The past decade has seen the great potential of applying deep neural network (DNN) based software to safety-critical scenarios, such as autonomous driving. Similar to traditional software, DNNs could exhibit incorrect behaviors, caused by hidden defects, leading to severe accidents and losses. In this paper, we propose DeepHunter, a coverage-guided fuzz testing framework for detecting potential defects of general-purpose DNNs. To this end, we first propose a new metamorphic mutation strategy to generate new semantically preserved tests, and leverage multiple extensible coverage criteria as feedback to guide the test generation. We further propose a new seed selection strategy that combines both diversity-based seed selection and recency-based seed selection. We finally implement 5 existing testing criteria and 4 seed selection strategies in DeepHunter. Large-scale experiments demonstrate that (1) the metamorphic mutation strategy is useful to generate new valid tests with the same semantics as the original seed, by a 98% validity ratio; (2) diversity-based seed selection is more important than recency-based seed selection in boosting the coverage and in detecting defects; (3) DeepHunter significantly outperforms the state of the art (TensorFuzz and DeepTest) by coverage as well as the quantity and diversity of defects identified; (4) using corner-region based criteria, DeepHunter tends to be more useful to capture defects (capture 4x more defects than TensorFuzz in MobileNet) during the DNN quantization for platform migration.

Thu 18 Jul

Displayed time zone: Beijing, Chongqing, Hong Kong, Urumqi change

14:00 - 15:30
Testing and Machine LearningTechnical Papers at Grand Ballroom
Chair(s): Hongyu Zhang The University of Newcastle
14:00
22m
Talk
DeepHunter: A Coverage-Guided Fuzz Testing Framework for Deep Neural Networks
Technical Papers
Xiaofei Xie Nanyang Technological University, Lei Ma Kyushu University, Felix Juefei-Xu Carnegie Mellon University, Minhui Xue , Hongxu Chen Nanyang Technological University, Yang Liu Nanyang Technological University, Singapore, Jianjun Zhao Kyushu University, Bo Li UIUC, Jianxiong Yin NVIDIA AI Tech Centre, Simon See NVIDIA AI Tech Centre
14:22
22m
Talk
Search-based Test and Improvement of Machine-Learning-Based Anomaly Detection SystemsArtifacts ReusableArtifacts Functional
Technical Papers
Maxime Cordy SnT, University of Luxembourg, Steve Muller unaffiliated, Mike Papadakis University of Luxembourg, Yves Le Traon University of Luxembourg
14:45
22m
Talk
DeepFL: Integrating Multiple Fault Diagnosis Dimensions for Deep Fault LocalizationArtifacts ReusableDistinguished Paper AwardsArtifacts Functional
Technical Papers
Xia Li University of Texas at Dallas, USA, Wei Li Southern University of Science and Technology, Yuqun Zhang Southern University of Science and Technology, Lingming Zhang
15:07
22m
Talk
Codebase-Adaptive Detection of Security-Relevant MethodsArtifacts Functional
Technical Papers
Goran Piskachev Fraunhofer IEM, Lisa Nguyen Quang Do Paderborn University, Eric Bodden Heinz Nixdorf Institut, Paderborn University and Fraunhofer IEM
DOI Pre-print Media Attached File Attached