Moshi Wei

Moshi Wei

Software Developer, Researcher

Bio

Hello, I am Moshi, a Ph.D. student in Software Engineering from York University. My research focuses on addressing the usability and reliability issues of deep-learning APIs, such as recommending and detecting misuse of deep-learning APIs. During my Ph.D. study, I have published several papers on deep-learning API recommendation and misuse detection in top-tier software engineering conferences (ICSE, ASE) and journals. Besides my research experience, I also have experience working in industry companies such as Huawei Canada and Ericsson. I am currently looking for professorial positions in Canada to continue my research on API usability and reliability.

Highlights

  • Publications in top venues: 3 ICSE, 3 TOSEM, 1 FSE, 1 TSE, 1 ISSTA
  • Research Interests: software engineering, machine learning, software reliability, software defect prediction, and software testing. Focusing on using deep learning, and language model techniques to improve system usability and reliability.

Education

Ph.D., Software Engineering      York University, Toronto, Ontario, Canada
Supervisor: Dr. Song Wang        Sep. 2020 – May. 2024

M.Sc., Software Engineering      University of Waterloo, Waterloo, Ontario, Canada
Supervisor: Dr. Lin Tan                Dec. 2017 – May. 2019

B.Sc., Computer Science            University of Ottawa, Ottawa, Ontario, Canada
Sep. 2012 – May. 2016

Publication

  • P1 Moshi Wei, Nima Shiri Harzevili, Yuekai Huang, Jinqiu Yang, Junjie Wang, and Song Wang.
    Demystifying and Detecting Misuses of Deep Learning APIs.
    44th International Conference on Software Engineering (ICSE 2024). 13 pages, CORE A*.

  • P2 Huang Yuekai, Wang Junjie, Wang Song, Wei Moshi, Shi Lin, Liu Zhe, and Wang Qing.
    Deep API Sequence Generation via Golden Solution Samples and API Seeds ACM Transactions on Software Engineering and Methodology (TOSEM 2024). 20 pages, CORE A*.

  • P3 Jiho Shin, Hadi Hemmati, Moshi Wei, and Song Wang.
    Assessing Evaluation Metrics for Neural Test Oracle Generation
    IEEE Transaction on Software Engineering (TSE 2024). 11 pages, CORE A*.

  • P4 Nima Shiri Harzevili, Mohammad Mahdi Mohajer, Moshi Wei, Hung Viet Pham, and Song Wang.
    History-Driven Fuzzing For Deep Learning Libraries
    ACM Transactions on Software Engineering and Methodology (TOSEM 2024). 28 pages, CORE A*.

  • P5 Mohammad Mahdi Mohajer, Reem Aleithan, Nima Shiri Harzevili, Moshi Wei, Alvine Boaye Belle, Hung Viet Pham, Song Wang.
    Effectiveness of ChatGPT for Static Analysis: How Far Are We?
    Proceedings of the 1st ACM International Conference on AI-Powered Software (AIware 2024) 10 pages.

  • P6 Jiho Shin, Moshi Wei, Junjie Wang, Lin Shi, and Song Wang.
    The Good, the Bad, and the Missing: Neural Code Generation for Machine Learning Tasks.
    ACM Transactions on Software Engineering and Methodology (TOSEM’23). 20 pages, CORE A*.

  • P7 Omar Alhussein, Moshi Wei, Arashmid Akhavain.
    Dynamic Encoding and Decoding of Information for Split Learning in Mobile-Edge Computing: Leveraging Information Bottleneck Theory.
    IEEE Global Communications Conference (Globecom’23). 7 pages.

  • P8 Moshi Wei, Yuchao Huang, Jinqiu Yang, Junjie Wang, and Song Wang.
    CoCoFuzzing: Testing Neural Code Models with Coverage-Guided Fuzzing.
    IEEE Transactions on Reliability (TR’22). 14 pages, CORE A.(Cited 10+ Times)

  • P9 Moshi Wei, Yuchao Huang, Junjie Wang, Jiho Shin, Shiri harzevili Nima, and Song Wang.
    API Recommendation for Machine Learning Libraries: How Far Are We?.
    The 15th Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering (ESEC/FSE 2022). 12 pages, CORE A*.

  • P10 Moshi Wei, Shiri harzevili Nima, Yuchao Huang, Junjie Wang, and Song Wang.
    CLEAR: Contrastive Learning for API Recommendation.
    44th International Conference on Software Engineering (ICSE2022). 12 pages, CORE A*. (Cited 23+ Times)

  • P11 Yuchao Huang, Moshi Wei, Song Wang, Junjie Wang, Qing Wang.
    Yet Another Combination of IR-and Neural-based Comment Generation.
    Information and Software Technology 2022 (IST 2022). 11 pages, CORE A.

  • P12 Song Wang, Nishtha Shrestha, Abarna Kucheri Subburaman, Junjie Wang,Moshi Wei, and Nachiappan Nagappan.
    Automatic Unit Test Generation for Machine Learning Libraries: How Far Are We?.
    43rd International Conference on Software Engineering (ICSE 2021). 12 pages, CORE A*. (Cited 24+ Times)

  • P13 Thibaud Lutellier, Hung Viet Pham, Lawrence Pang, Yitong Li, Moshi Wei, Lin Tan.
    Coconut: combining context-aware neural translation models using ensemble for program repair.
    Proceedings of the 29th ACM SIGSOFT international symposium on software testing and analysis (ISSTA 2020). 12 pages, CORE A*. (Cited 236+ Times)

Industrial Experience

Research Intern - International Strategy - Research Nov. 2023 – Now

Huawei Technologies Canada, Toronto, Ontario, CA

  • Responsible for advanced Software AI Agent research.

Research Intern - Advanced Wireless Capability - Jan. 2023 – May. 2023

Huawei Technologies Canada, Ottawa, Ontario, CA

  • Developed deep learning model for 6G signal transmission prototype.
  • Explored the impact of the Large Language Model on enterprise-level network configuration software.

Research Intern - R&D - Nov. 2021 – Jan. 2022

Ericsson Software, Toronto, Ontario, CA

  • Developed a prototype for a low-code application development platform for network service providers.

Machine Learning Engineer - Business Intelligence - May. 2019 – Jul. 2020

Achievers, Toronto, Ontario, CA

  • Designed and implemented a recommendation system for user-to-user recommendation.
  • Maintained a data warehouse for a professional development network application with employee data from 300+ public companies.