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

Displayed time zone: Tijuana, Baja California change

10:40 - 12:20
Deep ModelsResearch Papers / Demonstrations at Cortez 2&3
Chair(s): Nazareno Aguirre Dept. of Computer Science FCEFQyN, University of Rio Cuarto
10:40
20m
Talk
Wuji: Automatic Online Combat Game Testing Using Evolutionary Deep Reinforcement LearningACM SIGSOFT Distinguished Paper Award
Research Papers
Yan Zheng Tianjin University, Xiaofei Xie Nanyang Technological University, Ting Su ETH Zurich, Lei Ma Kyushu University, Jianye Hao Tianjin University, Zhaopeng Meng Tianjin University, Yang Liu Nanyang Technological University, Singapore, Ruimin Shen Fuxi AI Lab in Netease, Yinfeng Chen Fuxi AI Lab in Netease, Changjie Fan Fuxi AI Lab in Netease
Link to publication Pre-print
11:00
20m
Talk
A Study of Oracle Approximations in Testing Deep Learning Libraries
Research Papers
Mahdi Nejadgholi Concordia University, Jinqiu Yang Concordia University, Montreal, Canada
11:20
20m
Talk
Property Inference for Deep Neural Networks
Research Papers
Divya Gopinath Carnegie Mellon University, Hayes Converse The University of Texas at Austin, Corina S. Pasareanu Carnegie Mellon University Silicon Valley, NASA Ames Research Center, Ankur Taly Google
11:40
20m
Talk
An Empirical Study towards Characterizing Deep Learning Development and Deployment across Different Frameworks and Platforms
Research Papers
Qianyu Guo Tianjin University, Sen Chen Nanyang Technological University, Singapore, Xiaofei Xie Nanyang Technological University, Lei Ma Kyushu University, Qiang Hu Kyushu University, Japan, Hongtao Liu Tianjin University, Yang Liu Nanyang Technological University, Singapore, Jianjun Zhao Kyushu University, Li Xiaohong TianJin University
Pre-print
12:00
10m
Demonstration
DeepMutation++: a Mutation Testing Framework for Deep Learning Systems
Demonstrations
Qiang Hu Kyushu University, Japan, Lei Ma Kyushu University, Xiaofei Xie Nanyang Technological University, Bing Yu Kyushu University, Japan, Yang Liu Nanyang Technological University, Singapore, Jianjun Zhao Kyushu University
12:10
10m
Demonstration
DeepHunter: A Coverage-Guided Fuzzer for Deep Neural Networks
Demonstrations
Xiaofei Xie Nanyang Technological University, Hongxu Chen Nanyang Technological University, Yi Li Nanyang Technological University, Lei Ma Kyushu University, Yang Liu Nanyang Technological University, Singapore, Jianjun Zhao Kyushu University