Deep Learning Library Testing via Effective Model GenerationACM SIGSOFT Distinguished Paper Award
Deep learning (DL) techniques are rapidly developed and have been widely adopted in practice. However, similar to traditional software systems, DL systems also contain bugs, which could cause serious impacts especially in safety-critical domains. Recently, many research approaches have focused on testing DL models, while little attention has been paid for testing DL libraries, which is the basis of building DL models and directly affects the behavior of DL systems. In this work, we propose a novel approach, LEMON, to testing DL libraries. In particular, we (1) design a series of mutation rules for DL models, with the purpose of exploring different invoking sequences of library code and hard-to-trigger behaviors; and (2) propose a heuristic strategy to guide the model generation process towards the direction of amplifying the inconsistent degrees of the inconsistencies between different DL libraries caused by bugs, so as to mitigate the impact of potential noise introduced by uncertain factors in DL libraries. We conducted an empirical study to evaluate the effectiveness of LEMON with 20 release versions of 4 widely-used DL libraries, i.e., TensorFlow, Theano, CNTK, MXNet. The results demonstrate that LEMON detected 24 new bugs in the latest release versions of these libraries, where 7 bugs have been confirmed and one bug has been fixed by developers. Besides, the results confirm that the heuristic strategy for model generation indeed effectively guides LEMON in amplifying the inconsistent degrees for bugs.
Tue 10 NovDisplayed time zone: (UTC) Coordinated Universal Time change
01:30 - 02:00 | |||
01:30 2mTalk | Correlations between Deep Neural Network Model Coverage Criteria and Model Quality Research Papers Shenao Yan Rutgers University, USA, Guanhong Tao Purdue University, USA, Xuwei Liu Purdue University, USA, Juan Zhai Rutgers University, USA, Shiqing Ma Rutgers University, USA, Lei Xu Nanjing University, China, Xiangyu Zhang Purdue University DOI | ||
01:33 1mTalk | Deep Learning Library Testing via Effective Model GenerationACM SIGSOFT Distinguished Paper Award Research Papers Zan Wang Tianjin University, China, Ming Yan Tianjin University, China, Junjie Chen Tianjin University, China, Shuang Liu Tianjin University, China, Dongdi Zhang Tianjin University, China DOI | ||
01:35 1mTalk | Detecting Numerical Bugs in Neural Network ArchitecturesACM SIGSOFT Distinguished Paper Award Research Papers Yuhao Zhang Peking University, Luyao Ren Peking University, China, Liqian Chen National University of Defense Technology, China, Yingfei Xiong Peking University, Shing-Chi Cheung Hong Kong University of Science and Technology, China, Tao Xie Peking University DOI | ||
01:37 1mTalk | Dynamic Slicing for Deep Neural Networks Research Papers Ziqi Zhang Peking University, China, Yuanchun Li Microsoft Research, China, Yao Guo Peking University, Xiangqun Chen Peking University, Yunxin Liu Microsoft Research, China DOI | ||
01:39 1mTalk | Grammar Based Directed Testing of Machine Learning Systems Journal First Sakshi Udeshi Singapore University of Technology and Design, Sudipta Chattopadhyay Singapore University of Technology and Design | ||
01:41 1mTalk | Is Neuron Coverage a Meaningful Measure for Testing Deep Neural Networks? Research Papers Fabrice Harel-Canada University of California at Los Angeles, USA, Lingxiao Wang University of California at Los Angeles, USA, Muhammad Ali Gulzar University of California at Los Angeles, USA, Quanquan Gu University of California at Los Angeles, USA, Miryung Kim University of California at Los Angeles, USA DOI | ||
01:43 1mTalk | Operational Calibration: Debugging Confidence Errors for DNNs in the Field Research Papers Zenan Li Nanjing University, China, Xiaoxing Ma Nanjing University, China, Chang Xu Nanjing University, China, Jingwei Xu Nanjing University, China, Chun Cao Nanjing University, China, Jian Lu Nanjing University, China DOI | ||
01:45 15mTalk | Conversations on ML Testing 1 Research Papers Fabrice Harel-Canada University of California at Los Angeles, USA, Ming Yan Tianjin University, China, Sakshi Udeshi Singapore University of Technology and Design, Shenao Yan Rutgers University, USA, Yuhao Zhang Peking University, Zenan Li Nanjing University, China, Ziqi Zhang Peking University, China, M: Hamid Bagheri University of Nebraska-Lincoln, USA | ||