Guowei Yang
Texas State University
45 Papers
92 Citations
Guowei Yang is an academic researcher from Texas State University. The author has contributed to research in topics: Symbolic execution & Computer science. The author has an hindex of 13, co-authored 36 publications. Previous affiliations of Guowei Yang include Chinese Academy of Sciences & University of Texas at Austin.
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Papers
Directed Incremental Symbolic Execution
TL;DR: The novelty of DiSE is to combine the efficiencies of static analysis techniques to compute program difference information with the precision of symbolic execution to explore program execution paths and generate path conditions affected by the differences.
Audio-Visual Event Localization via Recursive Fusion by Joint Co-Attention
Bin Duan,Hao Tang,Wei Wang,Ziliang Zong,Guowei Yang,Yan Yan +5 more
- 01 Jan 2021
TL;DR: In this article, a joint co-attention mechanism with multi-modal fusion methods for audio-visual event localization is proposed, where visual features are combined with auditory features and then turned into joint representations.
TestEra: A tool for testing Java programs using alloy specifications
Shadi Abdul Khalek,Guowei Yang,Lingming Zhang,Darko Marinov,Sarfraz Khurshid +4 more
- 06 Nov 2011
TL;DR: This tool paper presents an embodiment of TestEra - a framework developed in previous work for specification-based testing of Java programs that uses the method's pre-condition specification to generate test inputs and the post-condition to check correctness of outputs.
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Property differencing for incremental checking
Guowei Yang,Sarfraz Khurshid,Suzette Person,Neha Rungta +3 more
- 31 May 2014
TL;DR: Experimental results in the context of symbolic execution of Java programs annotated with properties written as assertions show the effectiveness of iProperty in utilizing change information to enable more efficient checking.
Who Should Be Selected to Perform a Task in Crowdsourced Testing
Qiang Cui,Junjie Wang,Guowei Yang,Miao Xie,Qing Wang,Mingshu Li +5 more
- 04 Jul 2017
TL;DR: ExReDiv is introduced, a novel hybrid approach to select a set of workers for a test task that consists of three key strategies: the experience strategy selects experienced workers, the relevance strategy selects workers with expertise relevant to the given test task, and the diversity strategy selects diverse workers to avoid detecting duplicated bugs.
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