no code implementations • 16 Apr 2024 • Kaibo Liu, Yiyang Liu, Zhenpeng Chen, Jie M. Zhang, Yudong Han, Yun Ma, Ge Li, Gang Huang
Conventional automated test generation tools struggle to generate test oracles and tricky bug-revealing test inputs.
no code implementations • 8 Feb 2024 • QiPeng Wang, Shiqi Jiang, Zhenpeng Chen, Xu Cao, Yuanchun Li, Aoyu Li, Ying Zhang, Yun Ma, Ting Cao, Xuanzhe Liu
Additionally, we noticed that in-browser inference increases the time it takes for graphical user interface (GUI) components to load in web browsers by a significant 67. 2\%, which severely impacts the overall QoE for users of web applications that depend on this technology.
no code implementations • 5 Aug 2023 • Xinyue Li, Zhenpeng Chen, Jie M. Zhang, Federica Sarro, Ying Zhang, Xuanzhe Liu
This paper analyzes fairness in automated pedestrian detection, a crucial but under-explored issue in autonomous driving systems.
1 code implementation • 25 Jul 2023 • Zhenpeng Chen, Jie M. Zhang, Federica Sarro, Mark Harman
Existing research mostly improves the fairness of Machine Learning (ML) software regarding a single protected attribute at a time, but this is unrealistic given that many users have multiple protected attributes.
no code implementations • 14 Jul 2022 • Max Hort, Zhenpeng Chen, Jie M. Zhang, Mark Harman, Federica Sarro
How many datasets are used for evaluating bias mitigation methods?
2 code implementations • 7 Jul 2022 • Zhenpeng Chen, Jie M. Zhang, Federica Sarro, Mark Harman
We find that (1) the bias mitigation methods significantly decrease ML performance in 53% of the studied scenarios (ranging between 42%~66% according to different ML performance metrics); (2) the bias mitigation methods significantly improve fairness measured by the 4 used metrics in 46% of all the scenarios (ranging between 24%~59% according to different fairness metrics); (3) the bias mitigation methods even lead to decrease in both fairness and ML performance in 25% of the scenarios; (4) the effectiveness of the bias mitigation methods depends on tasks, models, the choice of protected attributes, and the set of metrics used to assess fairness and ML performance; (5) there is no bias mitigation method that can achieve the best trade-off in all the scenarios.
no code implementations • 19 Jul 2021 • Zhenpeng Chen, Yuan Li
Among 2D convolutional networks on point clouds, point-based approaches consume point clouds of fixed size directly.
no code implementations • 10 Feb 2021 • Xuan Lu, Wei Ai, Zhenpeng Chen, Yanbin Cao, Qiaozhu Mei
This paper studies how emojis, as non-verbal cues in online communications, can be used for such purposes and how the emotional signals in emoji usage can be used to predict future behavior of workers.
1 code implementation • 13 Jan 2021 • Zhenpeng Chen, Huihan Yao, Yiling Lou, Yanbin Cao, Yuanqiang Liu, Haoyu Wang, Xuanzhe Liu
In contrast, faults related to the deployment of DL models on mobile devices (named as deployment faults of mobile DL apps) have not been well studied.
no code implementations • 12 Jun 2020 • Chengxu Yang, Qipeng Wang, Mengwei Xu, Zhenpeng Chen, Kaigui Bian, Yunxin Liu, Xuanzhe Liu
Based on the data and the platform, we conduct extensive experiments to compare the performance of state-of-the-art FL algorithms under heterogeneity-aware and heterogeneity-unaware settings.
no code implementations • 2 May 2020 • Zhenpeng Chen, Yanbin Cao, Yuanqiang Liu, Haoyu Wang, Tao Xie, Xuanzhe Liu
Deep learning (DL) becomes increasingly pervasive, being used in a wide range of software applications.
Software Engineering
1 code implementation • 4 Jul 2019 • Zhenpeng Chen, Yanbin Cao, Xuan Lu, Qiaozhu Mei, Xuanzhe Liu
However, commonly used out-of-the-box sentiment analysis tools cannot obtain reliable results on SE tasks and the misunderstanding of technical jargon is demonstrated to be the main reason.
1 code implementation • 12 Dec 2018 • Xuan Lu, Yanbin Cao, Zhenpeng Chen, Xuanzhe Liu
We find that emojis are used by a considerable proportion of GitHub users.
Computers and Society Software Engineering
1 code implementation • 7 Jun 2018 • Zhenpeng Chen, Sheng Shen, Ziniu Hu, Xuan Lu, Qiaozhu Mei, Xuanzhe Liu
To tackle this problem, cross-lingual sentiment classification approaches aim to transfer knowledge learned from one language that has abundant labeled examples (i. e., the source language, usually English) to another language with fewer labels (i. e., the target language).