Office: LAS 1012A
Lassonde School of Engineering
York University
Toronto, ON, Canada M3J 1P3

Phone: 416-736-2100 ext.33939
Email: wangsong@yorku.ca

Google Scholar

NEWS

7th May. 2024:
One paper accepted to QRS'24.

4th May. 2024:
One paper accepted to AIWare'24.

4th May. 2024:
One paper accepted to AIRE'24.

15th April. 2024:
One paper accepted to FSE 2024.

7th March. 2024:
I get the TOSEM Distinguished Reviewer Award 2023 TOSEM.

21th Feb. 2024:
One paper accepted to Forge'24.

9th Feb. 2024:
Our survey on LLM for software testing is accepted to TSE.

1st Jan. 2024:
Will serve as an Associate Editor of ACM Transactions on Software Engineering (TOSEM)

5th Dec. 2023:
our APSEC'23 paper won a Distinguished Paper Award.

9th Oct. 2023:
Moshi's paper is accepted to ICSE'24 (Second Cycle).

6th Oct. 2023:
Jiho's paper is accepted to TOSEM.

24th August. 2023:
One paper accepted to ICSE'24 (First Cycle).

23rd August. 2023:
Jiho's paper is accepted to APSEC'23.

29th July. 2023:
Niam's paper is accepted to ISSRE'23.

15th March. 2023:
Niam's paper is accepted to MSR'23.

9th Dec. 2022:
Two papers accepted to ICSE'23.

Nov. 2022:
Will serve on program committee of CAIN'23 and MSR'23

8th Sept. 2022:
One paper accepted to IEEE TR.

28th July. 2022:
Our Arun has successfully defended his master's thesis!

LINKS

Lassonde, EECS
Gradudate Application
Top SE Conferences
Top SE Journals

SPONSORS





I am an Associate Professor at the Department of Electrical Engineering and Computer Science at York University. I work at the intersection of Software Engineering and Artificial Intelligence. More specifically, my research focuses on 1) taking advantage of AI technologies to address challenging issues of software reliability practices (AI for SE) and 2) developing software reliability assurance techniques to improve the reliability and security of AI infrastructure systems (SE for AI). My research interests include software engineering, software reliability, program analysis, software testing, and machine learning. The tools and techniques developed in my research have already helped detect hundreds of true bugs.



Looking for highly motivated and self-driven students
I have several cool projects, and I am looking for hard-working students to drive them! Email me if you're interested in working with me. More details are here Opportunities.

Selected Publications (Full List)


  • Demystifying and Detecting Misuses of Deep Learning APIs
    Moshi Wei, Nima Shiri Harzevili, Yuekai Huang, Jinqiu Yang, Junjie Wang, and Song Wang
    ICSE 2024 (acceptance rate=22% 234/1053)
    PDF

  • Software Testing with Large Language Model: Survey, Landscape, and Vision
    Junjie Wang, Yuchao Huang, Chunyang Chen, Zhe Liu, Song Wang, Qing Wang
    IEEE Transaction on Software Engineering (TSE 2024)
    PDF

  • ClarifyGPT: Empowering LLM-based Code Generation with Intention Clarification
    Fangwen Mu, Lin Shi, Song Wang, Zhuohao Yu, Binquan Zhang, Chenxue Wang, Shichao Liu, and Qing Wang
    FSE 2024 (acceptance rate=25% 121/483)
    PDF

  • The Good, the Bad, and the Missing: Neural Code Generation for Machine Learning Tasks
    Jiho Shin, Moshi Wei, Junjie Wang, Lin Shi, and Song Wang
    ACM Transactions on Software Engineering and Methodology (TOSEM'23)
    PDF

  • An Empirical Study on the Stability of Explainable Software Defect Prediction
    Jiho Shin, Reem Aleithan, Jaechang Nam, Junjie Wang, Nima Shiri Harzevili and Song Wang
    APSEC 2023 (acceptance rate=33% 43/128)
    PDF Distinguished Paper Award

  • Automatic Static Vulnerability Detection for Machine Learning Libraries: Are We There Yet?
    Nima Shiri Harzevili, Jiho Shin, Junjie Wang, Song Wang, and Nachiappan Nagappan
    ISSRE 2023 (acceptance rate=25% 62/247)
    PDF

  • Characterizing and Understanding Software Security Vulnerabilities in Machine Learning Libraries
    Nima Shiri Harzevili, Jiho Shin, Junjie Wang, Song Wang, and Nachiappan Nagappan
    MSR 2023 (acceptance rate=37% 43/115)
    PDF

  • Developer-Intent Driven Code Comment Generation
    Fangwen Mu, Xiao Chen, Lin Shi, Song Wang, and Qing Wang
    ICSE 2023 (acceptance rate=26% 209/796)
    PDF

  • CoCoFuzzing: Testing Neural Code Models with Coverage-Guided Fuzzing
    Moshi Wei, Yuchao Huang, Jinqiu Yang, Junjie Wang, and Song Wang
    IEEE Transactions on Reliability (TR'22)
    PDF

  • Automatic Comment Generation via Multi-Pass Deliberation
    Fangwen Mu, Xiao Chen, Lin Shi, Song Wang, and Qing Wang
    ASE 2022 (acceptance rate=22% 116/525)
    PDF

  • API Recommendation for Machine Learning Libraries: How Far Are We?
    Moshi Wei, Yuchao Huang, Junjie Wang, Jiho Shin, Shiri harzevili Nima, and Song Wang
    ESEC/FSE 2022 (acceptance rate=22% 99/449)
    PDF

  • Are We Building on the Rock? On the Importance of Data Preprocessing for Code Summarization
    Lin Shi, Fangwen Mu, Xiao Chen, Song Wang, Junjie Wang, Ye Yang, Ge Li, Xin Xia, and Qing Wang
    ESEC/FSE 2022 (acceptance rate=22% 99/449)
    PDF

  • CLEAR: Contrastive Learning for API Recommendation
    Moshi Wei, Shiri harzevili Nima, Yuchao Huang, Junjie Wang, and Song Wang
    ICSE 2022 (acceptance rate=26% 197/751)
    PDF

  • Find Bugs in Static Bug Finders
    Junjie Wang, Yuchao Huang, Song Wang, and Qing Wang
    ICPC 2022 (acceptance rate=41% 41/102)
    ACM SIGSOFT Distinguished Paper Award
    PDF

  • Automatic Unit Test Generation for Machine Learning Libraries: How Far Are We?
    Song Wang, Nishtha Shrestha, Abarna Kucheri Subburaman, Junjie Wang, Moshi Wei, and Nachiappan Nagappan
    ICSE 2021 (acceptance rate=22% 138/615)
    PDF

  • Context- and Fairness-aware In-process Crowdworker Recommendation
    Junjie Wang, Ye, Yang, Song Wang, Jun Hu, and Qing Wang
    ACM Transactions on Software Engineering and Methodology (TOSEM'21)
    PDF

  • Context-aware Personalized Crowdtesting Task Recommendation
    Junjie Wang, Ye Yang, Song Wang, Chunyang Chen, Dandan Wang, and Qing Wang
    IEEE Transaction on Software Engineering 2021
    PDF

  • Large-Scale Intent Analysis for Identifying Large-Review-Effort Code Changes
    Song Wang, Chetan Bansal, and Nachiappan Nagappan
    Information and Software Technology 2020
    PDF

  • Context-aware In-process Crowdworker Recommendation
    Junjie Wang, Ye Yang, Song Wang, Dandan Wang, and Qing Wang.
    ICSE 2020 (acceptance rate=21% 129/617)
    ACM SIGSOFT Distinguished Paper Award
    PDF

  • Deep Semantic Feature Learning for Software Defect Prediction
    Song Wang, Taiyue Liu, Jaechang Nam, and Lin Tan
    IEEE Transaction on Software Engineering 2018
    PDF