Yuan Huang（黄袁） Associate Professor, Doctoral Advisor E-mail: huangyuan5 (at) mail.sysu.edu.cn School of Software Engineering, Sun Yat-Sen University, China Other Sites: Chinese Homepage, Google Scholar, DBLP Office: Room 321, Comprehensive Laboratory Building, Zhuhai Campus, Sun Yat-Sen University

Biography

Research Background

• 07/2021 - Present, Associate Professor, School of Software Engineering, Sun Yat-Sen University
• 02/2019 - 06/2021, Post-doctoral, School of Computer Science and Engineering, Sun Yat-sen University
• 01/2018 - 01/2019, Research Assistant, Department of Computing, The Hong Kong Polytechnic University
• 09/2013 - 12/2017, Ph.D, School of Data and Computer Science, Sun Yat-sen University

News

• December 17, 2024，Towards improving the performance of comment generation models by using bytecode information was accepted at IEEE Transactions on Software Engineering (CCF A). The bytecode information is introduced to improve the accruccy of the comment generation models in this study.
• November 27，2024，TG-CUP: A Transformer and GNN Based Multi-Modal Comment Updating Method was accepted at ACM Transactions on Software Engineering and Methodology (CCF A). We propose a new outdated comment update model TG-CUP in this study.
• October 31, 2024, An Alternative to Code Comment Generation? Generating Comment from Bytecode was accepted at Information and Software Technology (CCF B). In this study, we propose a novel approach called ByteGen to generate comments from bytecode.
• August 7, 2024, An Empirical Study on Learning-based Techniques for Explicit and Implicit Commit Messages Generation was accepted at ASE 2024 (CCF A). This work studies the performance of the learning-based models on the task of explicit and implicit commit message generation.
• August 5, 2024, Deep Learning-based Software Engineering: Progress, Challenges, and Opportunities was accepted at SCIENCE CHINA-Information Sciences (CCF B). In this paper, we present the first task-oriented survey on deep learning-based software engineering. It covers twelve major software engineering subareas significantly impacted by deep learning techniques. Such subareas spread out the through the whole lifecycle of software development and maintenance, including requirements engineering, software development, testing, maintenance, and developer collaboration.
• July 7, 2024, Are your comments outdated? Toward automatically detecting code-comment consistency was accepted at JSEP 2024 (CCF B). This work proposed an automatic method for detecting code-comment consistency.
• June 17, 2024, Are the smart contracts on Q&A site reliable? was accepted at SPE 2024 (CCF B). In this article, we conduct an empirical study investigating the distribution of vulnerabilities and gas-inefficient patterns in Ethereum Stack Exchange.
• April 3, 2024, Ethereum Transaction Replay Platform Based on State-wise Account Input Data was accepted at IEEE Transactions on Services Computing (CCF A). In this study we propose an efficient transaction replay platform, EthReplayer, which can not only replay the historical transactions of Ethereum quickly and faithfully but also realize the modification of transactions to achieve the purpose of testing with actual transactions.
• March 5, 2024, Generative Software Engineering is posted on the arXiv. This paper presents a comprehensive literature review of generative tasks in software engineering by applying large language models (LLMs). URL: https://arxiv.org/abs/2403.02583.
• February 17, 2024, Do Code Summarization Models Process Too Much Information? Function Signature May Be All What Is Needed was accepted at ACM Transactions on Software Engineering and Methodology (CCF A). This study found that there is no significant difference in BLEU value between comments generated using the function signature and those generated using the entire function as input.
• February 1, 2024, The Sword of Damocles: Upgradeable Smart Contract in Ethereum was accepted at ICPC 2024 (CCF B). This paper explores two ways in which smart contracts can be upgraded, and finds one of them can be quietly upgraded to the detriment of users.
• January 23, 2024, JIT-Smart: A Multi-Task Learning Framework for Just-In-Time Defect Prediction and Localization was accepted at FSE/ESEC 2024 (CCF A). Unlike traditional methods, which usually model just-in-time defect prediction and localization as a two-stage task, JIT-Smart models the commit-level prediction task and the line-level localization task in a unified way. In the prediction phase, JIT-Smart outperforms 7 SOTA methods, and in the localization phase, JIT-Smart outperforms 3 SOTA methods.
• January 11, 2024, ESGen: Commit Message Generation Based on Edit Sequence of Code Change was accepted at ICPC 2024 (CCF B). This work was completed by Li Yangzi, an undergraduate student in our research group.
• January 04, 2024, An Empirical Study of Code Reuse between GitHub and Stack Overflow during Software Development was accepted at Journal of Systems and Software (CCF B). This paper is an extension version of the paper accepted by ICPC'22.
See More...
December 01, 2023, Smart Contract Code Repair Recommendation based on Reinforcement Learning and Multi-metric Optimization was accepted at ACM Transactions on Software Engineering and Methodology (CCF A).
November 18, 2023, Towards Automatically Identifying the Co-change of Production and Test Code was accepted at Software Testing, Verification & Reliability (CCF B).
July 03, 2023, Snippet Comment Generation Based on Code Context Expansion was accepted at ACM Transactions on Software Engineering and Methodology (CCF A).
June 12, 2023, An Empirical Study on Real Bug Fixes from Solidity Smart Contract Projects was accepted at Journal of Systems and Software (CCF B).
June 08, 2023, Studying Differentiated Code to Support Smart Contract Update was accepted at Empirical Software Engineering (CCF B). This paper is an extension version of the paper accepted by ICPC'19.
January 05, 2023, A Comparative Study on Method Comment and Inline Comment was accepted at ACM Transactions on Software Engineering and Methodology (CCF A). In this paper, we studied the task of automatic code comment generation, and revealed the equivalent mechanism of function-level external comment and internal comment, and explored the theoretical basis of building the automatic generation model of the internal comment.
December 16, 2022, Security Code Recommendations for Smart Contract was accepted at SANER'23. In this paper, we proposed SMARTREP, an end-to-end tool, for smart contract repair with one-line patches.
November 28, 2022, BCGen: A Comment Generation Method for Bytecode was accepted at Automated Software Engineering Journal. In this paper, we have initially explored the problem of code comment generation in scenarios where source code is not available, using bytecode as input to the generation model. The significance of this work is that code comment generation tools can be generalized from open source software to closed source software.
March 9, 2022, Towards Exploring the Code Reuse from Stack Overflow during Software Development was accepted at ICPC'22. In this paper, we conduct a comprehensive empirical study to learn how programmers reuse code on Stack Overflow during project development.
July 20, 2021, Reviewing rounds prediction for code patches was accepted at Empirical Software Engineering.

Publication (*Corresponding Author)

• Software Engineering
• Yuan Huang, Jinbo Huang, Xiangping Chen, Zibin Zheng, Towards improving the performance of comment generation models by using bytecode information, IEEE Transactions on Software Engineering (TSE ), 2024. [PDF]
• Yuan Huang, Jinyu Jiang, Xiapu Luo, Xiangping Chen, Zibin Zheng, and Gang Huang, Change-Patterns Mapping: A Boosting Way for Change Impact Analysis, IEEE Transactions on Software Engineering (TSE ), 2022. [PDF]
• Yuan Huang, Xiangping Chen, Kai Hong, Zibin Zheng, Code Review Knowledge Perception: Fusing Multi-Features for Salient-Class Location, IEEE Transactions on Software Engineering (TSE ), 2021. [PDF]
• Yuan Huang, Junhuai Shu, Xiangping Chen, Xiapu Luo, Zibin Zheng, A comparative study on method comment and inline comment, ACM Transactions on Software Engineering and Methodology (TOSEM ), 2023. [PDF]
• Hanyang Guo, Xiangping Chen, Yuan Huang, Yanlin Wang, Zibin Zheng. Snippet Comment Generation Based on Code Context Expansion. ACM Transactions on Software Engineering and Methodology, (TOSEM ), 2023. [PDF]
• Ding Xi, Peng Rui, Chen Xiangping, Yuan Huang*, Bian Jing, Zheng Zibin. Do Code Summarization Models Process Too Much Information? Function Signature May Be All What Is Needed. ACM Transactions on Software Engineering and Methodology, (TOSEM ), 2024. [PDF]
• Yinan Chen, Yuan Huang, Xiangping Chen, Zibin Zheng. TG-CUP: A Transformer and GNN Based Multi-Modal Comment Updating Method. ACM Transactions on Software Engineering and Methodology, (TOSEM ), 2024. [PDF]
• Yuan Huang, Xinyu Hu, Xiangping Chen, Yingfei Xiong, and Zibin Zheng. Learning code context information to predict comment locations. IEEE Transactions on Reliability (TR ), 2020. [PDF]
• Yuan Huang, Xingjian Liang, Xiapu Luo, Xiangping Chen, Zibin Zheng, Xiaocong Zhou, Reviewing Rounds Prediction for Code Patches, Empirical Software Engineering (EMSE ), 2022. [PDF]
• Yuan Huang, Jinbo Huang, Xiangping Chen, Kunning He, Xiaocong Zhou, BCGen: A Comment Generation Method for Bytecode , Automated Software Engineering (ASE Journal ), 2022. [PDF]
• Yuan Huang, Xinyu Hu, Xiangping Chen, Zibin Zheng, Xiapu Luo, CommtPst: Deep learning source code for commenting positions prediction, Journal of Systems and Software (JSS ), 2020.
• Yuan Huang, Shaohao Huang, Xiangping Chen, Zibin Zheng, Xiapu Luo, Xiaocong Zhou. Towards automatically generating block comments for code snippets, Information and Software Technology (IST ), 2020. [PDF]
• Yuan Huang, Haojie Zhou, Xiangping Chen, Zibin Zheng, Learning Human-Written Commit Messages to Document Code Changes, Journal of Computer Science and Technology (JCST ), 2020. [PDF]
• Yuan Huang, Yinan Chen, Xiangping Chen, Xiaocong Zhou, Are your comments outdated? Toward automatically detecting code-comment consistency. Journal of Software: Evolution and Process (JSEP), 2024.[PDF]
• Xiangping Chen, Xing Hu, Yuan Huang,et al. Deep Learning-based Software Engineering: Progress, Challenges, and Opportunities, SCIENCE CHINA Information Sciences (SCIS ), 2024. [PDF]
• Xiangping Chen, Furen Xu, Yuan Huang, An Empirical Study of Code Reuse between GitHub and Stack Overflow during Software Development, Journal of Systems and Software (JSS ), 2024.
• Xiangping Chen, Junqi Chen, Zhilu Lian, Yuan Huang*, Xiaocong Zhou, Zibin Zheng, An Alternative to Code Comment Generation? Generating Comment from Bytecode, Information and Software Technology (IST ), 2024.
• Huanchao Chen, Yuan Huang, Zhiyong Liu, Xiangping Chen, Fan Zhou, and Xiaonan Luo. Automatically detecting the scopes of source code comments. Journal of Systems and Software, 153:45 – 63, (JSS ), 2019. [PDF]
• Yuan Huang, Zhicao Tang, Xiangping Chen, Xiaocong Zhou, Towards automatically identifying the co-change of production and test code, Software Testing, Verification & Reliability (STVR ), 2023. [PDF]
• Yuan Huang, Junhuai Su, Xinyu Hu, Xiangping Chen and Qiang Zhou. Does Your Code Need Comment?. Software: Practice and Experience (SPE), 2020. [PDF]
• Yuan Huang, Xiangping Chen, Zhiyong Liu, Xiaonan Luo, and Zibin Zheng. Using discriminative feature in software entities for relevance identification of code changes. Journal of Software: Evolution and Process (JSEP), 2017. [PDF]
• Zhiquan Huang,Yuan Huang,Xiangping Chen,Xiaocong Zhou,Changlin Yang,Zibin Zheng. "An Empirical Study on Learning-based Techniques for Explicit and Implicit Commit Messages Generation, In Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering, ASE 2024. [PDF]
• Xiangping Chen,Furen Xu, Yuan Huang*, Neng Zhang, Zibin Zheng, "JIT-Smart: A Multi-Task Learning Framework for Just-In-Time Defect Prediction and Localization," In Proceedings of the 2024 ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, FSE/ESEC 2024. [PDF]
• Xiangping Chen,Yangzi Li, Zhicao Tang, Yuan Huang*, Haojie Zhou, Zibin Zheng, "ESGen: Commit Message Generation Based on Edit Sequence of Code Change," In IEEE/ACM 32th International Conference on Program Comprehension (ICPC), 2024. [PDF]
• Yuan Huang, Furen Xu, Haojie Zhou, Xiangping Chen, Xiaocong Zhou, "Towards Exploring the Code Reuse from Stack Overflow during Software Development," In IEEE/ACM 30th International Conference on Program Comprehension (ICPC), 2022. [PDF]
• Yuan Huang, Qiaoyang Zheng, Xiangping Chen, Yingfei Xiong, Zhiyong Liu, and Xiaonan Luo. Mining version control system for automatically generating commit comment. In 2017 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM), 2017.
• Yuan Huang, Nan Jia, Xiangping Chen, Kai Hong, and Zibin Zheng. Salient-class location: Help developers understand code change in code review. In Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/FSE 2018. pages 770–774. [PDF]
• Yuan Huang, Nan Jia, Xiangping Chen, Yingfei Xiong. (2018, May). Guiding developers to make informative commenting decisions in source code. In Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings (pp. 260-261).
• Yuan Huang, Xiangping Chen, Qiwen Zou, Xiaonan Luo. A probabilistic neural network-based approach for related software changes detection[C]//2014 21st Asia-Pacific Software Engineering Conference. IEEE, 2014, 1: 279-286.
• Qiwen Zou, Xiangping Chen, Yuan Huang. Topic Matching Based Change Impact Analysis from Feature on User Interface of Mobile Apps[C]//SEKE. 2015: 477-482.
• Blockchain & Smart Contract
• Yuan Huang, Rong Wang, Xiangping Chen,Zibin Zheng. Ethereum Transaction Replay Platform Based on State-wise Account Input Data. IEEE Transactions on Services Computing, (TSC ), 2024. [PDF]
• Hanyang Guo, Xiangping Chen,Yingye Chen, Yuan Huang, Zibin Zheng. Smart Contract Code Repair Recommendation based on Reinforcement Learning and Multi-metric Optimization. ACM Transactions on Software Engineering and Methodology, (TOSEM ), 2023. [PDF]
• Xiangping Chen, Peiyong Liao, Queping Kong, Yuan Huang*, and Xiaocong Zhou. Studying Differentiated Code to Support Smart Contract Update. Empirical Software Engineering (EMSE ), 2023. [PDF]
• Yilin Wang, Xiangping Chen,Yuan Huang*, Hao-Nan Zhu, Jing Bian, and Zibin Zheng. An Empirical Study on Real Bug Fixes from Solidity Smart Contract Projects. Journal of Systems and Software, (JSS ), 2023. [PDF]
• Queping Kong, Ziyan Wang, Yuan Huang, Xiangping Chen, Xiaocong Zhou, Zibin Zheng, Gang Huang. Characterizing and detecting gas-inefficient patterns in smart contracts. Journal of Computer Science and Technology, 37(1), 67-82, (JCST ), 2022. [PDF]
• Xiaocong Zhou, Quanqi Wang, Yifan Liu, Xiangping Chen, Yuan Huang, Zibin Zheng. Are the smart contracts on Q&A site reliable? Software: Practice and Experience. (SPE ), 2024. 1-20, doi: 10.1002/spe.3361 [PDF]
• Yuan Huang, Xiaoyuan Wu, Quanqi Wang, Ziang Qian, Xiangping Chen, Zibin Zheng. The Sword of Damocles: Upgradeable Smart Contract in Ethereum. In IEEE/ACM 32th International Conference on Program Comprehension (ICPC), 2024. [PDF]
• Yuan Huang, Queping Kong, Xiangping Chen, and Zibin Zheng. Recommending differentiated code to support smart contract update. In IEEE/ACM 27th International Conference on Program Comprehension (ICPC), 2019. [PDF]
• Xiangping Chen, Peiyong Liao, Yixin Zhang, Yuan Huang*, Zibin Zheng. Understanding Code Reuse in Smart Contracts. IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), 2021. [PDF]
• Xiaocong Zhou, Yingye Chen, Hanyang Guo, Xiangping Chen, Yuan Huang*, Security Code Recommendations for Smart Contract, IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), 2023. Accepted
• Yuan Huang, Rong Wang, Xiangping Chen, Xiaocong Zhou, Ziyan Wang. (2022). GOV: A Verification Method for Smart Contract Gas-Optimization. In 2022 IEEE 22st International Conference on Software Quality, Reliability and Security Companion (QRS).
• Ziyan Wang, Xiangping Chen, Xiaocong Zhou, Yuan Huang, Zibin Zheng. (2021, December). An empirical study of solidity language features. In 2021 IEEE 21st International Conference on Software Quality, Reliability and Security Companion (QRS) (pp. 698-707). [PDF]
• Xiangping Chen, Queping Kong, Haonan Zhu, Yixin Zhang, Yuan Huang*, & Zigui Jiang. (2020). Deciphering Cryptocurrencies by Reverse Analyzing on Smart Contracts. In Second International Conference Blockchain and Trustworthy Systems, 2020, Best Paper (pp. 532-546). [PDF]

Fundings

• National Natural Science Foundation of China（国家基金青年项目，主持）
• Guangdong Basic and Applied Basic Research Foundation（广东省基金面上，主持）
• China Postdoctoral Science Foundation（博士后基金，一等资助，主持）
• National Key R&D Program of China（国家重点研发，项目骨干）
• Guangdong Key Area R&D Program（广东省重点研发，项目骨干）

Journal/Conference Reviewers

• ACM Transactions on Software Engineering and Methodology (TOSEM)
• Journal of Software: Evolution and Process (JSME)
• Journal of Systems and Software (JSS)
• Software Testing, Verification and Reliability (STVR)
• Engineering Applications of Artificial Intelligence (EAAI)
• International Conference on Blockchain and Trustworthy Systems 2020
• ACM Symposium on Information, Computer and Communications Security 2020
• IEEE Conference on Communications and Network Security 2020

Awards (Selected)

• 2024: Excellent Master's Degree Thesis Advisor, In School of Software Engineering, Sun Yat-sen University, 2024
• 2020: Best Paper Award, In Blockchain and Trustworthy Systems: Second International Conference, BlockSys 2020
• 2019: Excellent Advisor Award, The first prize of the National University Blockchain Competition, 2019

Teaching

• 2022~: Blockchain Principles and Technologies (Undergraduate courses)
• 2021~: Software Advanced Technology and Application (Undergraduate courses)
• 2021~: Blockchain and Smart Contracts (Graduate Course)

Students