DeepCrime: from Real Faults to Mutation Testing Tool for Deep Learning
The recent advance of Deep Learning (DL) due to its human-competitive performance in complex and often safety-critical tasks, reveals many gaps in their testing. There exist a number of DL-specific testing approaches, and yet none has presented the possibility of simulating the occurrence of real DL faults for the mutation testing of DL systems. We propose 35 and implement 24 mutation operators that were systematically extracted from the existing studies on real DL faults. Our evaluation shows that the proposed operators produce non-redundant, killable, and non-trivial mutations while being more sensitive to the change in the quality of test data than the existing mutation testing approaches. Video demonstration is available at: https://youtu.be/WOvuPaXH6Jk
Thu 18 MayDisplayed time zone: Hobart change
11:00 - 12:30 | AI testing 1Technical Track / DEMO - Demonstrations / Journal-First Papers at Meeting Room 102 Chair(s): Matthew B Dwyer University of Virginia | ||
11:00 15mTalk | When and Why Test Generators for Deep Learning Produce Invalid Inputs: an Empirical Study Technical Track Pre-print | ||
11:15 15mTalk | Fuzzing Automatic Differentiation in Deep-Learning Libraries Technical Track Chenyuan Yang University of Illinois at Urbana-Champaign, Yinlin Deng University of Illinois at Urbana-Champaign, Jiayi Yao The Chinese University of Hong Kong, Shenzhen, Yuxing Tu Huazhong University of Science and Technology, Hanchi Li University of Science and Technology of China, Lingming Zhang University of Illinois at Urbana-Champaign | ||
11:30 15mTalk | Lightweight Approaches to DNN Regression Error Reduction: An Uncertainty Alignment Perspective Technical Track Zenan Li Nanjing University, China, Maorun Zhang Nanjing University, China, Jingwei Xu , Yuan Yao Nanjing University, Chun Cao Nanjing University, Taolue Chen Birkbeck University of London, Xiaoxing Ma Nanjing University, Jian Lu Nanjing University Pre-print | ||
11:45 7mTalk | DeepJudge: A Testing Framework for Copyright Protection of Deep Learning Models DEMO - Demonstrations Jialuo Chen Zhejiang University, Youcheng Sun The University of Manchester, Jingyi Wang Zhejiang University, Peng Cheng Zhejiang University, Xingjun Ma Deakin University | ||
11:52 7mTalk | DeepCrime: from Real Faults to Mutation Testing Tool for Deep Learning DEMO - Demonstrations | ||
12:00 7mTalk | DiverGet: a Search-Based Software Testing approach for Deep Neural Network Quantization assessment Journal-First Papers Ahmed Haj Yahmed École Polytechnique de Montréal, Houssem Ben Braiek École Polytechnique de Montréal, Foutse Khomh Polytechnique Montréal, Sonia Bouzidi National Institute of Applied Science and Technology, Rania Zaatour Potsdam Institute for Climate Impact Research | ||
12:07 15mTalk | Revisiting Neuron Coverage for DNN Testing: A Layer-Wise and Distribution-Aware Criterion Technical Track Yuanyuan Yuan The Hong Kong University of Science and Technology, Qi Pang HKUST, Shuai Wang Hong Kong University of Science and Technology | ||