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Jin Liu 0016
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Publications
- 2024
- [j57]Juncai Guo, Jin Liu, Xiao Liu, Li Li:
Summarizing source code through heterogeneous feature fusion and extraction. Inf. Fusion 103: 102058 (2024) - 2023
- [j53]Juncai Guo, Jin Liu, Xiao Liu, Yao Wan, Li Li:
Summarizing source code with Heterogeneous Syntax Graph and dual position. Inf. Process. Manag. 60(5): 103415 (2023) - [j52]Pei Liu, Qingxin Xia, Kui Liu, Juncai Guo, Xin Wang, Jin Liu, John C. Grundy, Li Li:
Towards automated Android app internationalisation: An exploratory study. J. Syst. Softw. 197: 111559 (2023) - 2022
- [c60]Juncai Guo, Jin Liu, Yao Wan, Li Li, Pingyi Zhou:
Modeling Hierarchical Syntax Structure with Triplet Position for Source Code Summarization. ACL (1) 2022: 486-500 - [c57]Xin Wang, Yasheng Wang, Yao Wan, Jiawei Wang, Pingyi Zhou, Li Li, Hao Wu, Jin Liu:
CODE-MVP: Learning to Represent Source Code from Multiple Views with Contrastive Pre-Training. NAACL-HLT (Findings) 2022: 1066-1077 - [i6]Xin Wang, Yasheng Wang, Yao Wan, Jiawei Wang, Pingyi Zhou, Li Li, Hao Wu, Jin Liu:
CODE-MVP: Learning to Represent Source Code from Multiple Views with Contrastive Pre-Training. CoRR abs/2205.02029 (2022) - 2021
- [j37]Zhou Xu, Li Li, Meng Yan, Jin Liu, Xiapu Luo, John Grundy, Yifeng Zhang, Xiaohong Zhang:
A comprehensive comparative study of clustering-based unsupervised defect prediction models. J. Syst. Softw. 172: 110862 (2021) - [c50]Xin Wang, Xiao Liu, Li Li, Xiao Chen, Jin Liu, Hao Wu:
Time-aware User Modeling with Check-in Time Prediction for Next POI Recommendation. ICWS 2021: 125-134 - [c48]Kunsong Zhao, Jin Liu, Zhou Xu, Li Li, Meng Yan, Jiaojiao Yu, Yuxuan Zhou:
Predicting Crash Fault Residence via Simplified Deep Forest Based on A Reduced Feature Set. ICPC 2021: 242-252 - [i4]Kunsong Zhao, Jin Liu, Zhou Xu, Li Li, Meng Yan, Jiaojiao Yu, Yuxuan Zhou:
Predicting Crash Fault Residence via Simplified Deep Forest Based on A Reduced Feature Set. CoRR abs/2104.01768 (2021) - 2020
- [c43]Xin Wang, Jin Liu, Li Li, Xiao Chen, Xiao Liu, Hao Wu:
Detecting and Explaining Self-Admitted Technical Debts with Attention-based Neural Networks. ASE 2020: 871-882
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