L
Lei Ma
Researcher at University of Alberta
Publications - 241
Citations - 5126
Lei Ma is an academic researcher from University of Alberta. The author has contributed to research in topics: Computer science & Geology. The author has an hindex of 26, co-authored 130 publications receiving 2855 citations. Previous affiliations of Lei Ma include Nanyang Technological University & Kyushu University.
Papers
More filters
Proceedings ArticleDOI
DeepGauge: multi-granularity testing criteria for deep learning systems
Lei Ma,Felix Juefei-Xu,Fuyuan Zhang,Jiyuan Sun,Minhui Xue,Bo Li,Chunyang Chen,Ting Su,Li Li,Yang Liu,Jianjun Zhao,Yadong Wang +11 more
TL;DR: DeepGauge is proposed, a set of multi-granularity testing criteria for DL systems, which aims at rendering a multi-faceted portrayal of the testbed and sheds light on the construction of more generic and robust DL systems.
Journal ArticleDOI
Machine Learning Testing: Survey, Landscapes and Horizons
TL;DR: A comprehensive survey of machine learning testing can be found in this article, which covers 138 papers on testing properties (e.g., correctness, robustness, and fairness), testing components (i.e., the data, learning program, and framework), testing workflow, and application scenarios.
Proceedings ArticleDOI
DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems
Lei Ma,Felix Juefei-Xu,Fuyuan Zhang,Jiyuan Sun,Minhui Xue,Bo Li,Chunyang Chen,Ting Su,Li Li,Yang Liu,Jianjun Zhao,Yadong Wang +11 more
TL;DR: DeepGauge as discussed by the authors proposes a set of multi-granularity testing criteria for DL systems, which aims at rendering a multi-faceted portrayal of the testbed.
Proceedings ArticleDOI
DeepHunter: a coverage-guided fuzz testing framework for deep neural networks
Xiaofei Xie,Lei Ma,Felix Juefei-Xu,Minhui Xue,Hongxu Chen,Yang Liu,Jianjun Zhao,Bo Li,Jianxiong Yin,Simon See +9 more
TL;DR: DeepHunter, a coverage-guided fuzz testing framework for detecting potential defects of general-purpose DNNs, is proposed and a metamorphic mutation strategy to generate new semantically preserved tests is proposed, and multiple extensible coverage criteria as feedback to guide the test generation.
Proceedings ArticleDOI
DeepMutation: Mutation Testing of Deep Learning Systems
Lei Ma,Lei Ma,Fuyuan Zhang,Jiyuan Sun,Minhui Xue,Bo Li,Felix Juefei-Xu,Chao Xie,Li Li,Yang Liu,Jianjun Zhao,Yadong Wang +11 more
TL;DR: This paper proposes a mutation testing framework specialized for DL systems to measure the quality of test data, and designs a set of model-level mutation operators that directly inject faults into DL models without a training process.