conf.researchr.org / Shangwen Wang
Registered user since Thu 30 Jan 2020
Name:Shangwen Wang
Bio:
I am currently an assistant professor in the School of Computer Science, National University of Defense Technology (NUDT). I have obtained my Master degree and Doctor degree in December 2019 and December 2023 respectively from the National University of Defense Technology. During my studies, I was honored and fortunate to be supervised by Prof. Xiaoguang Mao. My research interests mainly focus on program repair, program comprehension, mining software repositories, software maintenance and evolution, software testing, and AI for SE.
Country:China
Affiliation:National University of Defense Technology
Personal website: https://shangwenwang.github.io/
X (Twitter): https://x.com/SharvenW
GitHub: https://github.com/ShangwenWang
Research interests:Program repair, Program Comprehension, Mining Software Repository, Software Maintenance and Evolution, Software Testing, AI for SE
Contributions
2025
2024
ASE
ICSE
- Author of When Neural Code Completion Models Size up the Situation: Attaining Cheaper and Faster Completion through Dynamic Model Inference within the Research Track-track
- Author of Large Language Models are Few-Shot Summarizers: Multi-Intent Comment Generation via In-Context Learning within the Research Track-track
- Committee Member in Artifact Evaluation within the Artifact Evaluation-track
International Conference on Program Comprehension
Mining Software Repositories
SANER
2023
Mining Software Repositories
ESEC/FSE
- Author of [Remote] Natural Language to Code: How Far are We? within the Research Papers-track
- Author of [Remote] An Extensive Study on Adversarial Attack against Pre-trained Models of Code within the Research Papers-track
- Author of [Remote] CCT5: A Code-Change-Oriented Pre-Trained Model within the Research Papers-track
ICSE
- Author of Don't Complete It! Preventing Unhelpful Code Completion for Productive and Sustainable Neural Code Completion Systems within the Posters-track
- Author of Predictive Comment Updating with Heuristics and AST-Path-Based Neural Learning: A Two-Phase Approach within the Journal-First Papers-track
International Conference on Program Comprehension
2022
ASE
- Author of Is this Change the Answer to that Problem? Correlating Descriptions of Bug and Code Changes for Evaluating Patch Correctness within the Research Papers-track
- Author of Is this Change the Answer to that Problem?Correlating Descriptions of Bug and Code Changes for Evaluating Patch Correctness within the Artifact Evaluation-track
- Author of Reentrancy Vulnerability Detection and Localization: A Deep Learning Based Two-phase Approach within the Research Papers-track