Wei Ma

Wei Ma

A Coder, A Researcher, A Scientist

About Wei Ma, Ph.D.

Currently, I am a research scientist at Singapore Management University (SMU). I have published over 20 papers in top-tier conferences and journals, including ICSE, ISSTA, ASE, TOSEM, and EMSE. During my Ph.D. studies at University of Luxembourg, my research focused on enhancing the efficiency and practicality of mutation testing, through a systematic investigation of its fundamental theories and enabling techniques, working closely with Prof. Yves Le Traon and Prof. Mike Papadakis. In my postdoctoral work, I broadened my research scope to encompass interdisciplinary topics at the intersection of software testing and intelligent system security, resulting in a diversified and forward-looking research portfolio. At SMU, I work with Prof. Lingxiao Jiang, and during my time at Nanyang Technological University (NTU) in Singapore, I collaborated with Prof. Yang Liu and Prof. Yi Li. I served as a reviewer for top conferences and journals, including ISSTA, TOSEM, ASE NIER, JSS, JSA, KNOSYS, and EMSE.

I am currently on the academic job market seeking faculty positions. If you are interested in my research or potential collaboration, please feel free to contact me at weima@smu.edu.sg or weima93@gmail.com.

Research Interests

  • Software Testing (especially the design and application of mutation testing and fuzz testing methods)
  • Trustworthy AI (focusing on robustness evaluation and interpretability analysis)
  • Syntax-Semantic Modeling and Controllability Analysis of Code Foundation Models
  • Software Security and Governance Mechanisms in Web3 systems

Academic Appointments

  • Research Scientist, Singapore Management University (Oct. 2024 – Present)
  • Research Fellow, Nanyang Technological University (Oct. 2022 – Oct. 2024)
  • Research Assistant, University of Luxembourg (May 2022 – Sep. 2023)

Publication

Please click my Google Scholar to check my full publications. * co-first author, # corresponding author

Dynamic Testing

My research in dynamic testing focuses on enhancing the efficiency and practicality of mutation testing and fuzz testing methodologies. I develop novel approaches for commit-aware mutation testing, delta-oriented testing strategies, and intelligent fuzz driver generation to improve software quality assurance processes.

  1. Wei Ma; Thierry Titcheu Chekam; Mike Papadakis; Mark Harman; MuDelta: Delta-Oriented Mutation Testing at Commit Time, the 43rd International Conference on Software Engineering (ICSE 2021), Madrid, Spain.
  2. Wei Ma; T. Laurent; M. Ojdanić; T. T. Chekam; A. Ventresque and M. Papadakis. Commit-Aware Mutation Testing, 2020 IEEE International Conference on Software Maintenance and Evolution Adelaide, SA, Australia, 2020, pp. 394-405, (ICSME ‘20, IEEE Computer Society TCSE Distinguished Paper Award)
  3. Miloš Ojdanić; Wei Ma; Thomas Laurent; Thierry Titcheu Chekam; Anthony Ventresque; and Mike Papadakis. 2022. On the use of commit-relevant mutants. Empirical Softw. Engg. 27, 5 (Sep 2022). https://doi.org/10.1007/s10664-022-10138-1 (EMSE ‘22)
  4. Xu, Hanxiang; Wei Ma; Ting Zhou; Yanjie Zhao; Kai Chen; Qiang Hu; Yang Liu; and Haoyu Wang. A Code Knowledge Graph-Enhanced System for LLM-Based Fuzz Driver Generation. (ICSE-Industry-Track, Best Paper Award, 2025)
  5. Cen Zhang; Yaowen Zheng; Mingqiang Bai; Yeting Li; Wei Ma; Xiaofei Xie; Yuekang Li; Limin Sun; and Yang Liu. 2024. How Effective Are They? Exploring Large Language Model Based Fuzz Driver Generation. In Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis Association for Computing Machinery, New York, NY, USA, 1223–1235 (ISSTA ‘24)

AI Testing and Security

I investigate the robustness and security of AI systems, particularly focusing on test selection methods for deep neural networks, adversarial attacks on code models, and security defenses for LLMs. My work addresses critical challenges in ensuring the reliability and trustworthiness of AI-powered software systems.

  1. Wei Ma; Mike Papadakis; Anestis Tsakmalis; Maxime Cordy; Yves Le Traon; Test Selection for Deep Learning Systems, ACM Transactions on Software Engineering and Methodology, 2021, 30(2): 1-22.
  2. Zhihao Lin; Wei Ma; Mingyi Zhou; Yanjie Zhao; Haoyu Wang; Yang Liu; Jun Wang; Li Li; MazeBreaker: Multi-Agent Reinforcement Learning for Dynamic Jailbreaking of LLM Security Defenses, 48th ICSE, Brazil, 2026.
  3. Qiang Hu; Yuejun Guo; Xiaofei Xie; Maxime Cordy; Wei Ma#; Mike Papadakis; Lei Ma; and Yves Le Traon. 2025. Assessing the Robustness of Test Selection Methods for Deep Neural Networks. ACM Trans. Softw. Eng. Methodol. Just Accepted (January 2025, TOSEM ‘25). https://doi.org/10.1145/3715693
  4. Jie Zhang; Wei Ma#; Qiang Hu; Shangqing Liu; Xiaofei Xie; Yves Le Traon; and Yang Liu. 2023. A Black-Box Attack on Code Models via Representation Nearest Neighbor Search. In Findings of the Association for Computational Linguistics: Empirical Methods in Natural Language Processing, 2023, pages 9706–9716, Singapore. Association for Computational Linguistics. (EMNLP-Findings ‘23)
  5. Dang, X.; Li, Y.; Wei Ma. et al. Towards Exploring the Limitations of Test Selection Techniques on Graph Neural Networks: An Empirical Study. Empirical Software Engineering 29, 112 (2024). (EMSE ‘24)
  6. Hu, Qiang; Yuejun Guo; Maxime Cordy; Xiaofei Xie; Wei Ma; Mike Papadakis; and Yves Le Traon. “Towards Understanding Model Quantization for Reliable Deep Neural Network Deployment.” In 2023 IEEE/ACM 2nd International Conference on AI Engineering–Software Engineering for AI (CAIN), pp. 56-67. IEEE, 2023.
  7. Qiang Hu; Yuejun Guo; Maxime Cordy; Xiaofei Xie; Wei Ma; Mike Papadakis; and Yves Le Traon. 2022. Towards exploring the limitations of active learning: an empirical study. In Proceedings of the 36th IEEE/ACM International Conference on Automated Software Engineering IEEE Press, 917–929.(ASE ‘22)

Software and Web3 Security

My research explores security challenges in decentralized systems and Web3 applications. I focus on governance issues in DeFi protocols, smart contract auditing using LLM-based agents, vulnerability detection methods, and comprehensive analysis of security threats in blockchain ecosystems.

  1. Wei Ma; Chenguang Zhu; Ye Liu; Xiaofei Xie; Yi Li; A Comprehensive Study of Governance Issues in Decentralized Finance Applications, ACM Transactions on Software Engineering and Methodology, 2025.
  2. Wei Ma; Daoyuan Wu; Yuqiang Sun; Tianwen Wang; Shangqing Liu; Jian Zhang; Yue Xue; Yang Liu; Combining Fine-tuning and LLM-based Agents for Intuitive Smart Contract Auditing with Justifications, IEEE/ACM 47th International Conference on Software Engineering (ICSE 2025), Ottawa, Canada.
  3. Sun, Dianxiang; Wei Ma; Liming Nie; and Yang Liu. Sok: Comprehensive analysis of rug pull causes, datasets, and detection tools in defi. (ISSTA 2025)
  4. Shangqing Liu; Wei Ma#; Jian Wang; Xiaofei Xie; Ruitao Feng; and Yang Liu. 2024. Enhancing Code Vulnerability Detection via Vulnerability-Preserving Data Augmentation. In Proceedings of the 25th ACM SIGPLAN/SIGBED International Conference on Languages, Compilers, and Tools for Embedded Systems (LCTES 2024). Association for Computing Machinery, New York, NY, USA, 166–177. https://doi.org/10.1145/3652032.3657564

The Foundation and Application of Large Language Model for Code

I study the fundamental capabilities and practical applications of LLMs in software engineering. My research investigates syntax and semantic understanding in code pre-trained models, develops generic code embedding techniques, and explores collaborative software learning through AI-based tools for program understanding and automated debugging.

  1. Wei Ma; Shangqing Liu; Mengjie Zhao; Xiaofei Xie; Wenhang Wang; Qiang Hu; Jie Zhang; Yang Liu; Unveiling Code Pre-Trained Models: Investigating Syntax and Semantics Capacities, ACM TOSEM, 2024, 33(7): 1-29.
  2. Wei Ma; Mengjie Zhao; Ezekiel Soremekun; Qiang Hu; Jie M. Zhang; Mike Papadakis; Maxime Cordy; Xiaofei Xie; and Yves Le Traon. 2022. GraphCode2Vec: generic code embedding via lexical and program dependence analyses. In Proceedings of the 19th International Conference on Mining Software Repositories Association for Computing Machinery, New York, NY, USA, 524–536. (MSR ‘22)
  3. Zhihao Lin; Wei Ma; Tao Lin; Yaowen Zheng; Jingquan Ge; Jun Wang; Jacques Klein; Tegawende Bissyande; Yang Liu; and Li Li. 2024. Open-Source AI-based SE Tools: Opportunities and Challenges of Collaborative Software Learning. ACM Trans. Softw. Eng. Methodol. Just Accepted (December 2024). https://doi.org/10.1145/3708529 (TOSEM ‘25)
  4. Junjie Shi; Wei Ma#; Chen Chi; Lingxiao Jiang. Debugging For LLM-Based Software (FSE 2030 Workshop, FSE’ 2025)
  5. Z. Lin; M. Zhou; Wei Ma; C. Chen; Y. Yang; J. Wang; C. Hu; L. Li. HomeRepair: Learn to Repair OpenHarmony Apps, (FSE 2025 Industry Track, Accept)
  6. Shangqing Liu; Yanzhou Li; Xiaofei Xie; Wei Ma#; Guozhu Meng; and Yang Liu. 2024. Automated Commit Intelligence by Pre-training. ACM Trans. Softw. Eng. Methodol. 33, 8, Article 201 (November 2024), 30 pages. https://doi.org/10.1145/3674731 (TOSEM ‘25)
  7. Wenhan Wang; Yanzhou Li; Anran Li; Jian Zhang; Wei Ma; and Yang Liu. 2024. An Empirical Study on Noisy Label Learning for Program Understanding. In Proceedings of the IEEE/ACM 46th International Conference on Software Engineering. Association for Computing Machinery, New York, NY, USA, Article 95, 1–12.(ICSE ‘24)