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Shangwen Wang
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2020 – today
- 2024
- [j9]Shangwen Wang
, Mingyang Geng
Fusing Code Searchers. IEEE Trans. Software Eng. 50(7): 1852-1866 (2024) - [c29]Mingyang Geng
Large Language Models are Few-Shot Summarizers: Multi-Intent Comment Generation via In-Context Learning. ICSE 2024: 39:1-39:13 - [c28]Zhensu Sun
When Neural Code Completion Models Size up the Situation: Attaining Cheaper and Faster Completion through Dynamic Model Inference. ICSE 2024: 75:1-75:12 - [c27]Bo Lin
One Size Does Not Fit All: Multi-granularity Patch Generation for Better Automated Program Repair. ISSTA 2024: 1554-1566 - [c26]Zhang Zhang
CAREER: Context-Aware API Recognition with Data Augmentation for API Knowledge Extraction. ICPC 2024: 438-449 - [i17]Zhensu Sun, Xiaoning Du, Fu Song, Shangwen Wang, Li Li:
When Neural Code Completion Models Size up the Situation: Attaining Cheaper and Faster Completion through Dynamic Model Inference. CoRR abs/2401.09964 (2024) - [i16]Yihao Qin, Shangwen Wang, Yiling Lou, Jinhao Dong, Kaixin Wang, Xiaoling Li, Xiaoguang Mao:
AgentFL: Scaling LLM-based Fault Localization to Project-Level Context. CoRR abs/2403.16362 (2024) - [i15]Xiaopeng Li, Shangwen Wang, Shezheng Song, Bin Ji, Huijun Liu, Shasha Li, Jun Ma, Jie Yu:
Identifying Knowledge Editing Types in Large Language Models. CoRR abs/2409.19663 (2024) - 2023
- [j8]Shangwen Wang
Two Birds with One Stone: Boosting Code Generation and Code Search via a Generative Adversarial Network. Proc. ACM Program. Lang. 7(OOPSLA2): 486-515 (2023) - [j7]Shangwen Wang
Pre-implementation Method Name Prediction for Object-oriented Programming. ACM Trans. Softw. Eng. Methodol. 32(6): 157:1-157:35 (2023) - [j6]Bo Lin
Predictive Comment Updating With Heuristics and AST-Path-Based Neural Learning: A Two-Phase Approach. IEEE Trans. Software Eng. 49(4): 1640-1660 (2023) - [c25]Zhensu Sun, Xiaoning Du, Fu Song, Shangwen Wang, Mingze Ni, Li Li:
Don't Complete It! Preventing Unhelpful Code Completion for Productive and Sustainable Neural Code Completion Systems. ICSE Companion 2023: 324-325 - [c24]Kang Yang
An Extensive Study of the Structure Features in Transformer-based Code Semantic Summarization. ICPC 2023: 89-100 - [c23]Mingyang Geng, Shangwen Wang, Dezun Dong, Haotian Wang, Shaomeng Cao, Kechi Zhang, Zhi Jin:
Interpretation-based Code Summarization. ICPC 2023: 113-124 - [c22]Shangwen Wang
Natural Language to Code: How Far Are We? ESEC/SIGSOFT FSE 2023: 375-387 - [c21]Xiaohu Du
An Extensive Study on Adversarial Attack against Pre-trained Models of Code. ESEC/SIGSOFT FSE 2023: 489-501 - [c20]Bo Lin
CCT5: A Code-Change-Oriented Pre-trained Model. ESEC/SIGSOFT FSE 2023: 1509-1521 - [i14]Mingyang Geng, Shangwen Wang, Dezun Dong, Haotian Wang, Ge Li, Zhi Jin, Xiaoguang Mao, Xiangke Liao:
An Empirical Study on Using Large Language Models for Multi-Intent Comment Generation. CoRR abs/2304.11384 (2023) - [i13]Bo Lin, Shangwen Wang, Zhongxin Liu, Yepang Liu, Xin Xia, Xiaoguang Mao:
CCT5: A Code-Change-Oriented Pre-Trained Model. CoRR abs/2305.10785 (2023) - [i12]Ying Xiao, Shangwen Wang, Sicen Liu, Dingyuan Xue, Xian Zhan, Yepang Liu:
FITNESS: A Causal De-correlation Approach for Mitigating Bias in Machine Learning Software. CoRR abs/2305.14396 (2023) - [i11]Xiaohu Du, Ming Wen, Zichao Wei, Shangwen Wang, Hai Jin:
An Extensive Study on Adversarial Attack against Pre-trained Models of Code. CoRR abs/2311.07553 (2023) - [i10]Kang Yang, Xinjun Mao, Shangwen Wang, Tanghaoran Zhang, Bo Lin, Yanlin Wang, Yihao Qin, Zhang Zhang, Xiaoguang Mao:
Enhancing Code Intelligence Tasks with ChatGPT. CoRR abs/2312.15202 (2023) - 2022
- [j5]Steffen Herbold
A fine-grained data set and analysis of tangling in bug fixing commits. Empir. Softw. Eng. 27(6): 125 (2022) - [j4]Bo Lin
Context-Aware Code Change Embedding for Better Patch Correctness Assessment. ACM Trans. Softw. Eng. Methodol. 31(3): 51:1-51:29 (2022) - [c19]Yihao Qin, Shangwen Wang, Kui Liu, Bo Lin, Hongjun Wu, Li Li, Xiaoguang Mao, Tegawendé F. Bissyandé:
Peeler: Learning to Effectively Predict Flakiness without Running Tests. ICSME 2022: 257-268 - [c18]Mingyang Geng, Shangwen Wang
Fine-grained code-comment semantic interaction analysis. ICPC 2022: 585-596 - [c17]Haoye Tian
Is this Change the Answer to that Problem?: Correlating Descriptions of Bug and Code Changes for Evaluating Patch Correctness. ASE 2022: 59:1-59:13 - [c16]Zhuo Zhang, Yan Lei
Reentrancy Vulnerability Detection and Localization: A Deep Learning Based Two-phase Approach. ASE 2022: 83:1-83:13 - [c15]Menghan Wu, Yang Zhang, Jiakun Liu, Shangwen Wang, Zhang Zhang, Xin Xia, Xinjun Mao:
On the Way to Microservices: Exploring Problems and Solutions from Online Q&A Community. SANER 2022: 432-443 - [c14]Yinyuan Zhang, Yang Zhang, Xinjun Mao, Yiwen Wu, Bo Lin, Shangwen Wang:
Recommending Base Image for Docker Containers based on Deep Configuration Comprehension. SANER 2022: 449-453 - [i9]Haoye Tian
Is this Change the Answer to that Problem? Correlating Descriptions of Bug and Code Changes for Evaluating Patch Correctness. CoRR abs/2208.04125 (2022) - [i8]Zhensu Sun, Xiaoning Du, Fu Song, Shangwen Wang, Mingze Ni, Li Li:
Learning to Prevent Profitless Neural Code Completion. CoRR abs/2209.05948 (2022) - 2021
- [c13]Hongjun Wu, Zhuo Zhang, Shangwen Wang
Peculiar: Smart Contract Vulnerability Detection Based on Crucial Data Flow Graph and Pre-training Techniques. ISSRE 2021: 378-389 - [c12]Bo Lin, Shangwen Wang
Automated Comment Update: How Far are We? ICPC 2021: 36-46 - [c11]Shangwen Wang
Lightweight global and local contexts guided method name recommendation with prior knowledge. ESEC/SIGSOFT FSE 2021: 741-753 - [c10]Yihao Qin, Shangwen Wang
On the Impact of Flaky Tests in Automated Program Repair. SANER 2021: 295-306 - [i7]Shangwen Wang, Kui Liu, Bo Lin, Li Li, Jacques Klein, Xiaoguang Mao, Tegawendé F. Bissyandé:
Beep: Fine-grained Fix Localization by Learning to Predict Buggy Code Elements. CoRR abs/2111.07739 (2021) - 2020
- [c9]Bo Lin, Shangwen Wang
Understanding the Non-Repairability Factors of Automated Program Repair Techniques. APSEC 2020: 71-80 - [c8]Zhang Zhang
An Empirical Study on the Influence of Social Interactions for the Acceptance of Answers in Stack Overflow. APSEC 2020: 425-434 - [c7]Kui Liu
On the efficiency of test suite based program repair: A Systematic Assessment of 16 Automated Repair Systems for Java Programs. ICSE 2020: 615-627 - [c6]Shangwen Wang
Automated Patch Correctness Assessment: How Far are We? ASE 2020: 968-980 - [i6]Kui Liu, Shangwen Wang, Anil Koyuncu, Kisub Kim, Tegawendé F. Bissyandé, Dongsun Kim, Peng Wu, Jacques Klein, Xiaoguang Mao, Yves Le Traon:
On the Efficiency of Test Suite based Program Repair: A Systematic Assessment of 16 Automated Repair Systems for Java Programs. CoRR abs/2008.00914 (2020) - [i5]Steffen Herbold, Alexander Trautsch, Benjamin Ledel, Alireza Aghamohammadi, Taher Ahmed Ghaleb, Kuljit Kaur Chahal, Tim Bossenmaier, Bhaveet Nagaria, Philip Makedonski, Matin Nili Ahmadabadi, Kristóf Szabados, Helge Spieker, Matej Madeja, Nathaniel Hoy, Valentina Lenarduzzi, Shangwen Wang, Gema Rodríguez-Pérez, Ricardo Colomo Palacios, Roberto Verdecchia, Paramvir Singh, Yihao Qin, Debasish Chakroborti, Willard Davis, Vijay Walunj, Hongjun Wu, Diego Marcilio, Omar Alam, Abdullah Aldaeej, Idan Amit, Burak Turhan, Simon Eismann, Anna-Katharina Wickert, Ivano Malavolta, Matús Sulír, Fatemeh H. Fard, Austin Z. Henley, Stratos Kourtzanidis, Eray Tuzun, Christoph Treude, Simin Maleki Shamasbi
Large-Scale Manual Validation of Bug Fixing Commits: A Fine-grained Analysis of Tangling. CoRR abs/2011.06244 (2020)
2010 – 2019
- 2019
- [j3]Shangwen Wang
Dissection on Java Organs in GitHub Repositories. IEEE Access 7: 62561-62576 (2019) - [j2]Jiangtao Xue
History-Driven Fix for Code Quality Issues. IEEE Access 7: 111637-111648 (2019) - [c5]Shangwen Wang
Attention Please: Consider Mockito when Evaluating Newly Proposed Automated Program Repair Techniques. EASE 2019: 260-266 - [c4]Shangwen Wang
How Different Is It Between Machine-Generated and Developer-Provided Patches? : An Empirical Study on the Correct Patches Generated by Automated Program Repair Techniques. ESEM 2019: 1-12 - [c3]Shangwen Wang
Multi-Location Program Repair Strategies Learned from Successful Experience (S). SEKE 2019: 713-777 - [i4]Shangwen Wang, Ming Wen, Liqian Chen, Xin Yi, Xiaoguang Mao:
How Different Is It Between Machine-Generated and Developer-Provided Patches? An Empirical Study on The Correct Patches Generated by Automated Program Repair Techniques. CoRR abs/1906.03447 (2019) - 2018
- [j1]Shangwen Wang
An Initial Step Towards Organ Transplantation Based on GitHub Repository. IEEE Access 6: 59268-59281 (2018) - [c2]Anbang Guo, Xiaoguang Mao, Deheng Yang, Shangwen Wang
An Empirical Study on the Effect of Dynamic Slicing on Automated Program Repair Efficiency. ICSME 2018: 554-558 - [c1]Shangwen Wang
A Hybrid Approach for Tag Hierarchy Construction. ICSR 2018: 59-75 - [i3]Shangwen Wang, Xiaoguang Mao, Yue Yu:
An Initial Step Towards Organ Transplantation Based on GitHub Repository. CoRR abs/1810.04825 (2018) - [i2]Shangwen Wang, Xiaoguang Mao, Nan Niu, Xin Yi, Anbang Guo:
Multi-Location Program Repair: Roads Ahead. CoRR abs/1810.12556 (2018) - [i1]Shangwen Wang, Ming Wen, Deheng Yang, Xiaoguang Mao:
Attention Please: Consider Mockito when Evaluating Newly Released Automated Program Repair Techniques. CoRR abs/1812.05370 (2018)
Coauthor Index
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last updated on 2024-10-21 20:25 CEST by the dblp team
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