VLDB 2026 Research / reviewers in the wild / expert
Thanh Trong Vu
dblp:330/0979
· DBLP profile ↗
6ranked-venue papers
3as first author
6since 2021 · last 2026
0009-0008-3377-6565ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 6 · 3 first-author · 6 since 2021
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Model-agnostic quality assessment for LLM-generated code via dynamic internal representation selection
Thanh Trong Vu, Tuan-Dung Bui, Thu-Trang Nguyen, Hieu Dinh Vo |
J. Syst. Softw. | 1 |
| 2025 | An empirical study on capability of Large Language Models in understanding code semantics
Thu-Trang Nguyen, Thanh Trong Vu, Hieu Dinh Vo |
Inf. Softw. Technol. | 2 |
| 2025 | Automated description generation for software patches
Thanh Trong Vu, Tuan-Dung Bui, Thanh-Dat Do, Thu-Trang Nguyen, Hieu Dinh Vo |
Inf. Softw. Technol. | 1 |
| 2025 | Correctness assessment of code generated by Large Language Models using internal representations
Tuan-Dung Bui, Thanh Trong Vu, Thu-Trang Nguyen, Hieu Dinh Vo |
J. Syst. Softw. | 2 |
| 2024 | Context-Encoded Code Change Representation for Automated Commit Message GenerationabstractChanges in source code are an inevitable part of software development. They are the results of indispensable activities such as fixing bugs or improving functionality. Descriptions for code changes (commit messages) help people better understand the changes. However, due to the lack of motivation and time pressure, writing high-quality commit messages remains reluctantly considered. Several methods have been proposed with the aim of automated commit message generation. However, the existing methods are still limited because they only utilize either the changed codes or the changed codes combined with their surrounding statements. This paper proposes a method to represent code changes by combining the changed codes and the unchanged codes which have program dependence on the changed codes. Specifically, we first create program dependence graphs (PDGs) of source code before and after the change. After that, slices related to the changed code from these PDGs are extracted. These slices are then merged to represent the change. This method overcomes the limitations of current representations while improving the performance of 5/6 of state-of-the-art commit message generation methods by up to 15% in METEOR, 14% in ROUGE-L, and 10% in BLEU-4. Thanh Trong Vu, Thanh-Dat Do, Hieu Dinh Vo |
Int. J. Softw. Eng. Knowl. Eng. | 1 |
| 2024 | Code-centric learning-based just-in-time vulnerability detection
Thu-Trang Nguyen, Thanh Trong Vu, Thanh-Dat Do, Kien-Tuan Ngo, Hieu Dinh Vo |
J. Syst. Softw. | 3 |