For conference ranking, please refer to:

• Portal Ranking: The Computing Research and Education Association of Australasia.
• China Computer Federation (CCF).
• Top Computer Science Conferences.
• Google Scholar Metrics.
  

Flagship (A*): ICSE(3), FSE(1), ASE(3), AAAI(1), SIGIR(1), ACL(2), EMNLP(3)     Top (A): SANER(1), EACL(1)

Arxiv

• CodeTF: One-Stop Transformer-based Library for CodeLLMs, by Nghi D. Q. Bui, Henry Le, Yue Wang, Akhilesh Deepak Gotmare, Junnan Li, Steven Hoi.
  [PDF] [Code]
  
  
• RepoHyper: Better Context Retrieval Is All You Need for Repository-Level Code Completion, by Huy N. Phan, Hoang N. Phan, Tien N. Nguyen, Nghi D. Q. Bui.
  [PDF] [Code]
  
  
• Neural Rankers for Code Generation via Inter-Cluster Modeling, by Hung Quoc To, Minh Huynh Nguyen, Nghi D. Q. Bui.
  [PDF] [Code]
  

Journal Papers

• On the Generalizability of Neural Program Analyzers with respect to Semantic-Preserving Program Transformation, by Md. Rafiqul Islam RABIN, Nghi D. Q. BUI, Yijun YU, Lingxiao JIANG, Mohammad Amin ALIPOUR, accepted at the Journal of Information and Software Technology, 2020.
  [PDF]

Conference Papers

• [EACL’24] HierarchyNet: Learning to Summarize Source Code with Heterogeneous Representations, by Minh H. Nguyen, Nghi D. Q. Bui, Truong Son Hy, Long Tran-Thanh, Tien N. Nguyen, Long Findings, in Proceedings of 18th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2024), Malta.
  (Rank A) [PDF] [Code]
  
  
• [EMNLP’23] The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation, by Dung Nguyen Manh, Nam Le Hai, Anh T. V. Dau, Anh Minh Nguyen, Khanh Nghiem, Jin Guo, Nghi D. Q. Bui , Long Findings, in Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, Singapore.
  (Rank A*) [PDF] [Code]
  
  
• [EMNLP’23] CodeT5+: Open Code Large Language Models for Code Understanding and Generation, by Yue Wang, Hung Le, Akhilesh Deepak Gotmare, Nghi D. Q. Bui, Junnan Li, Steven Hoi, Long Main, in Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, Singapore.
  (Rank A*) [PDF] [Code]
  
  
• [ACL’23] Better Language Models of Code through Self-Improvement, by Hung Quoc To, Nghi D. Q. BUI (Co-first Author), Jin Guo, Tien N. Nguyen , Short Findings, in Proceedings of The 61st Annual Meeting of the Association for Computational Linguistics, Toronto, Canada.
  (Rank A*) [PDF] - Acceptance Rate: 19.1% (189/992)
  
  
• [ACL’23] Class-based Influence Functions for Error Detection, by Nguyen Duc-Thang, Hoang Thanh Tung, Quan Tran, Huu Tien Dang, Nguyen Ngoc Hieu, Anh T.V. Dau, Nghi D. Q. BUI , Short Main, in Proceedings of The 61st Annual Meeting of the Association for Computational Linguistics, Toronto, Canada.
  (Rank A*) [PDF] - Acceptance Rate: 19.1% (189/992)
  
  
• [EMNLP’22] Detect-Localize-Repair: A Unified Framework for Learning to Debug with CodeT5, by Nghi D. Q. BUI, Yue Wang, Steven Hoi, in Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Full Paper in Findings Track, Abu Dhabi, United Arab Emirates, 2022.
  (Rank A*) [PDF]- Acceptance Rate: 14.0% (453/3242)
  
  
• [ASE’22] Towards Using Data-Centric Approach for Better Code Representation Learning*, by Anh T.V Dau (Co-first Author), Nghi D. Q. BUI (Co-first Author), Thang Nguyen Duc, Hoang Thanh Tung, , in Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering, Late-Breaking Results Track, 2022, Michigan, US.
  (Rank A*) [PDF]
  
  
• [ASE’22] Towards Robust Models of Code via Energy-Based Learning on Auxiliary Datasets, by Nghi D. Q. BUI, Yijun Yu, in Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering, 2022, Late-Breaking Results Track, Michigan, US.
  (Rank A*) [PDF]
  
  
• [SIGIR’21] Self-Supervised Learning for Code Retrieval and Summarization through Semantic-Preserving Program Transformations, by Nghi D. Q. BUI, Yijun YU, Lingxiao JIANG, in Proceedings of the 44th ACM Conference on Research and Development in Information Retrieval (SIGIR), Full Paper, 2021.
  (Rank A*) [PDF]- Acceptance Rate: 21% (151/720)
  
  
• [ICSE’21] InferCode: Self-Supervised Learning of Code Representations by Predicting Subtrees, by Nghi D. Q. BUI, Yijun YU, Lingxiao JIANG, in Proceedings of the IEEE/ACM 43th International Conference on Software Engineering, Full Paper, Technical Track, Madrid, Spain, 2021.
  (Rank A*) [PDF] [Code]- Acceptance Rate: 22.9% (138/602)
  
  
• [AAAI’21] TreeCaps:Tree-based Capsule Networks for Source Code Processing, by Nghi D. Q. BUI, Yijun YU, Lingxiao JIANG, in Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI), Full Paper, Main Track, Vancouver, Canada, 2021.
  (Rank A*) [PDF] [Code]- Acceptance Rate: 21% (1692/7911)
  
  
• [ESEC/FSE’19] SAR: Learning Cross-Language API Mappings with Little Knowledge, by Nghi D. Q. BUI, Yijun YU, Lingxiao JIANG, in Proceedings of the IEEE/ACM 27th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE), Research Track, Tallinn, Estonia, 2019.
  (Rank A*) [PDF] [Code] - Acceptance Rate: 24.4% (74/303)
  
  
• [SANER’19] Bilateral Dependency Neural Networks for Cross-Language Algorithm Classification, by Nghi D. Q. BUI, Yijun YU, Lingxiao JIANG, in the 26th edition of the IEEE/ACM International Conference on Software Analysis, Evolution and Reengineering, Research Track, Hangzhou, China, 2019.
  (Rank A) [PDF] [Code] - Acceptance Rate: 27% (40/148)
  
  
• [ICSE’19] Towards Zero Knowledge Learning for Cross Language API Mappings, by Nghi D. Q. BUI, in Proceedings of the IEEE/ACM 41th International Conference on Software Engineering: ACM Student Research Competition Track (SRC), Montreal, Canada, 2019.
  (Rank A*) (Award: Bronze Medal) [PDF] [Poster] - Acceptance Rate: 45.2% (19/42)
  
  
• [ASE’19] AutoFocus: Interpreting Attention-based Neural Networks by Code Perturbation, by Nghi D. Q. BUI, Yijun YU, Lingxiao JIANG, in Proceedings of the 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), New Ideas Papers, San Diego, California, United States, 2019.
  (Rank A*) [PDF] - Acceptance Rate: 22.7% (93/409)
  
  
• [ICSE’18] Hierarchical Learning of Cross-Language Mappings through Distributed Vector Representations for Code, by Nghi D. Q. BUI, Lingxiao JIANG, in Proceedings of the IEEE/ACM 40th International Conference on Software Engineering: New Ideas and Emerging Technologies Results Track (NIER), Gothenburg, Sweden, 2018.
  (Rank A*) (Award: ACM SIGSOFT Distinguished Paper Award) [PDF] [Code] - Acceptance Rate: 26.3% (25/95)

Workshop Papers

• [MLSys-NeurIPS’19] TreeCaps: Tree-Structured Capsule Networks for Program Source Code Processing, by Vinoj JAYASUNDARA, Nghi D. Q. BUI, Lingxiao JIANG, David LO. In NeurIPS Workshop on ML for Systems 2019.
  
  
• [NL4SE-AAAI’18] Cross-Language Learning for Program Classification Using Bilateral Tree-Based Convolutional Neural Networks, by Nghi D. Q. BUI, Lingxiao JIANG, and Yijun YU. In the proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI) Workshop on Natural Language Processing for Software Engineering (NL4SE), New Orleans, Lousiana, USA, 2018. [PDF] [Code]