Blogs (1) >>
ASE 2019
Sun 10 - Fri 15 November 2019 San Diego, California, United States
Thu 14 Nov 2019 11:00 - 11:20 at Cortez 2&3 - Deep Models Chair(s): Nazareno Aguirre

Due to the popularity of deep learning (DL) applications, testing DL libraries are becoming more and more important. Different from traditional testing, for which output is asserted definitely (e.g., an output is compared with an oracle for equality), testing deep learning libraries often requires to perform oracle approximations, i.e., the output is allowed to be within a restricted range of the oracle. However, oracle approximations have not been studied in prior empirical work that focuses on traditional testing practices. The prevalence, common practices, evolution, and maintenance challenges of oracle approximations remain unknown. In this work, we studied oracle approximation assertions that are implemented in four popular deep learning libraries. Our study shows that oracle approximation assertions are a significant portion among all the assertions in the test suites of deep learning libraries. We identify the commonly-used oracle types when there are approximations being performed on oracles through a comprehensive manual study. In addition, we find that developers frequently modify code on oracle approximations, i.e., using a different approximation API, modifying the oracle or the output from the code under test, and using a different threshold value. Finally, we performed in-depth studies to understand the reasons behind the evolution of oracle approximation assertions and our findings reveal maintenance challenges.

Thu 14 Nov

10:40 - 12:20: Papers - Deep Models at Cortez 2&3
Chair(s): Nazareno AguirreDept. of Computer Science FCEFQyN, University of Rio Cuarto
ase-2019-papers10:40 - 11:00
Wuji: Automatic Online Combat Game Testing Using Evolutionary Deep Reinforcement LearningACM SIGSOFT Distinguished Paper Award
Yan ZhengTianjin University, Xiaofei XieNanyang Technological University, Ting SuETH Zurich, Lei MaKyushu University, Jianye HaoTianjin University, Zhaopeng MengTianjin University, Yang LiuNanyang Technological University, Singapore, Ruimin ShenFuxi AI Lab in Netease, Yinfeng ChenFuxi AI Lab in Netease, Changjie FanFuxi AI Lab in Netease
ase-2019-papers11:00 - 11:20
A Study of Oracle Approximations in Testing Deep Learning Libraries
Mahdi NejadgholiConcordia University, Jinqiu YangConcordia University, Montreal, Canada
ase-2019-papers11:20 - 11:40
Property Inference for Deep Neural Networks
Divya GopinathCarnegie Mellon University, Hayes ConverseThe University of Texas at Austin, Corina S. PasareanuCarnegie Mellon University Silicon Valley, NASA Ames Research Center, Ankur TalyGoogle
ase-2019-papers11:40 - 12:00
An Empirical Study towards Characterizing Deep Learning Development and Deployment across Different Frameworks and Platforms
Qianyu GuoTianjin University, Sen ChenNanyang Technological University, Singapore, Xiaofei XieNanyang Technological University, Lei MaKyushu University, Qiang HuKyushu University, Japan, Hongtao LiuTianjin University, Yang LiuNanyang Technological University, Singapore, Jianjun ZhaoKyushu University, Li XiaohongTianJin University
ase-2019-Demonstrations12:00 - 12:10
DeepMutation++: a Mutation Testing Framework for Deep Learning Systems
Qiang HuKyushu University, Japan, Lei MaKyushu University, Xiaofei XieNanyang Technological University, Bing YuKyushu University, Japan, Yang LiuNanyang Technological University, Singapore, Jianjun ZhaoKyushu University
ase-2019-Demonstrations12:10 - 12:20
DeepHunter: A Coverage-Guided Fuzzer for Deep Neural Networks
Xiaofei XieNanyang Technological University, Hongxu ChenNanyang Technological University, Yi LiNanyang Technological University, Lei MaKyushu University, Yang LiuNanyang Technological University, Singapore, Jianjun ZhaoKyushu University