Boosting the Revealing of Detected Violations in Deep Learning Testing: A Diversity-Guided MethodVirtualACM SIGSOFT Distinguished Paper Award
Due to the ability to bypass the oracle problem, Metamorphic Testing (MT) has been a popular technique to test deep learning (DL) software. However, no work has taken notice of the prioritization for Metamorphic test case Pairs (MPs), which is quite essential and beneficial to the effectiveness of MT in DL testing. When the fault-sensitive MPs apt to trigger violations and expose defects are not prioritized, the revealing of some detected violations can be greatly delayed or even missed to conceal critical defects. In this paper, we propose the first method to prioritize the MPs for DL software, so as to boost the revealing of detected violations in DL testing. Specifically, we devise a new type of metric to measure the execution diversity of DL software on MPs based on the distribution discrepancy of the neuron outputs. The fault-sensitive MPs are next prioritized based on the devised diversity metric. Comprehensive evaluation results show that the proposed prioritization method and diversity metric can effectively prioritize the fault-sensitive MPs, boost the revealing of detected violations, and even facilitate the selection and design of the effective Metamorphic Relations for the image classification DL software.
Thu 13 OctDisplayed time zone: Eastern Time (US & Canada) change
10:00 - 12:00 | Technical Session 21 - SE for AI IIResearch Papers / Late Breaking Results / NIER Track / Journal-first Papers at Banquet B Chair(s): Andrea Stocco Università della Svizzera italiana (USI) | ||
10:00 20mResearch paper | DeepPerform: An Efficient Approach for Performance Testing of Resource-Constrained Neural Networks Research Papers Simin Chen University of Texas at Dallas, USA, Mirazul Haque UT Dallas, Cong Liu University of Texas at Dallas, USA, Wei Yang University of Texas at Dallas | ||
10:20 10mPaper | Prototyping Deep Learning Applications with Non-Experts: An Assistant Proposition Late Breaking Results Gustavo Rodrigues dos Reis Rodrigues dos Reis, Adrian Mos NAVER LABS Europe, Cyril Labbé LIG - UGA, Mario Cortes Cornax LIG - UGA | ||
10:30 20mResearch paper | Boosting the Revealing of Detected Violations in Deep Learning Testing: A Diversity-Guided MethodVirtualACM SIGSOFT Distinguished Paper Award Research Papers Xiaoyuan Xie School of Computer Science, Wuhan University, China, Pengbo Yin School of Computer Science, Wuhan University, Songqiang Chen School of Computer Science, Wuhan University | ||
10:50 20mPaper | Faults in Deep Reinforcement Learning Programs: A Taxonomy and A Detection ApproachVirtual Journal-first Papers Amin Nikanjam École Polytechnique de Montréal, Mohammad Mehdi Morovati École Polytechnique de Montréal, Foutse Khomh Polytechnique Montréal, Houssem Ben Braiek École Polytechnique de Montréal Link to publication DOI Authorizer link | ||
11:10 20mResearch paper | Towards Understanding the Faults of JavaScript-Based Deep Learning SystemsVirtual Research Papers Lili Quan Tianjin University, Qianyu Guo College of Intelligence and Computing, Tianjin University, Xiaofei Xie Singapore Management University, Singapore, Sen Chen Tianjin University, Xiaohong Li TianJin University, Yang Liu Nanyang Technological University | ||
11:30 10mVision and Emerging Results | An Empirical Study on Numerical Bugs in Deep Learning ProgramsVirtual NIER Track Gan Wang , Zan Wang Tianjin University, China, Junjie Chen Tianjin University, Xiang Chen Nantong University, Ming Yan College of Intelligence and Computing, Tianjin University | ||
11:40 20mResearch paper | Toward Improving the Robustness of Deep Learning Models via Model TransformationVirtual Research Papers Yingyi Zhang College of Intelligence and Computing, Tianjin University, Zan Wang Tianjin University, China, Jiajun Jiang Tianjin University, Hanmo You College of Intelligence and Computing, Tianjin University, Junjie Chen Tianjin University | ||