References of "Ma, Wei 50034673"
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See detailGraphCode2Vec: generic code embedding via lexical and program dependence analyses
Ma, Wei UL; Zhao, Mengjie; Soremekun, Ezekiel UL et al

in Proceedings of the 19th International Conference on Mining Software Repositories (2022, May 22)

Code embedding is a keystone in the application of machine learn- ing on several Software Engineering (SE) tasks. To effectively support a plethora of SE tasks, the embedding needs to capture program ... [more ▼]

Code embedding is a keystone in the application of machine learn- ing on several Software Engineering (SE) tasks. To effectively support a plethora of SE tasks, the embedding needs to capture program syntax and semantics in a way that is generic. To this end, we propose the first self-supervised pre-training approach (called GraphCode2Vec) which produces task-agnostic embedding of lexical and program dependence features. GraphCode2Vec achieves this via a synergistic combination of code analysis and Graph Neural Networks. GraphCode2Vec is generic, it allows pre-training, and it is applicable to several SE downstream tasks. We evaluate the effectiveness of GraphCode2Vec on four (4) tasks (method name prediction, solution classification, mutation testing and overfitted patch classification), and compare it with four (4) similarly generic code embedding baselines (Code2Seq, Code2Vec, CodeBERT, Graph- CodeBERT) and seven (7) task-specific, learning-based methods. In particular, GraphCode2Vec is more effective than both generic and task-specific learning-based baselines. It is also complementary and comparable to GraphCodeBERT (a larger and more complex model). We also demonstrate through a probing and ablation study that GraphCode2Vec learns lexical and program dependence features and that self-supervised pre-training improves effectiveness. [less ▲]

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See detailTest Selection for Deep Learning Systems
Ma, Wei UL; Papadakis, Mike UL; Tsakmalis, Anestis et al

in ACM Transactions on Software Engineering and Methodology (2021), 30(2), 131--1322

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See detailMuDelta: Delta-Oriented Mutation Testing at Commit Time
Ma, Wei UL; Thierry Titcheu, Chekam; Papadakis, Mike UL et al

in International Conference on Software Engineering (ICSE) (2021)

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See detailCommit-Aware Mutation Testing
Ma, Wei UL; Laurent, Thomas; Ojdanic, Milos UL et al

in IEEE International Conference on Software Maintenance and Evolution (ICSME) (2020)

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See detailTechnology consumption and cognitive control: Contrasting action video game experience with media multitasking.
Cardoso-Leite, Pedro UL; Kludt, Rachel; Vignola, Gianluca et al

in Attention, perception & psychophysics (2016), 78(1), 218-41

Technology has the potential to impact cognition in many ways. Here we contrast two forms of technology usage: (1) media multitasking (i.e., the simultaneous consumption of multiple streams of media, such ... [more ▼]

Technology has the potential to impact cognition in many ways. Here we contrast two forms of technology usage: (1) media multitasking (i.e., the simultaneous consumption of multiple streams of media, such a texting while watching TV) and (2) playing action video games (a particular subtype of video games). Previous work has outlined an association between high levels of media multitasking and specific deficits in handling distracting information, whereas playing action video games has been associated with enhanced attentional control. Because these two factors are linked with reasonably opposing effects, failing to take them jointly into account may result in inappropriate conclusions as to the impacts of technology use on attention. Across four tasks (AX-continuous performance, N-back, task-switching, and filter tasks), testing different aspects of attention and cognition, we showed that heavy media multitaskers perform worse than light media multitaskers. Contrary to previous reports, though, the performance deficit was not specifically tied to distractors, but was instead more global in nature. Interestingly, participants with intermediate levels of media multitasking sometimes performed better than both light and heavy media multitaskers, suggesting that the effects of increasing media multitasking are not monotonic. Action video game players, as expected, outperformed non-video-game players on all tasks. However, surprisingly, this was true only for participants with intermediate levels of media multitasking, suggesting that playing action video games does not protect against the deleterious effect of heavy media multitasking. Taken together, these findings show that media consumption can have complex and counterintuitive effects on attentional control. [less ▲]

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