Song Wang

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

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

Google Scholar

NEWS

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

23th 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!

20th July. 2022:
One paper accepted to ASE 2022.

July. 2022:
Will serve on program committee of ESEC/FSE'23, SANER'23 , and ICSE'23 Demo.

5th July. 2022:
One paper accepted to IST.

17th June. 2022:
One paper accepted to ESEM 2022.

14th June. 2022:
Two papers accepted to ESEC/FSE 2022.

18th May. 2022:
One paper accepted to IEEE TR.

15th May. 2022:
our ICPC 2022 paper won an ACM SIGSOFT Distinguished Paper Award

8th Mar. 2022:
One paper accepted to ICPC'22.

LINKS

Lassonde, EECS
Gradudate Application
Top SE Conferences
Top SE Journals

SPONSORS





I am an Assistant Professor at the Department of Electrical Engineering and Computer Science at York University. I work at the intersection of Software Engineering and Machine Learning. More specifically, my research work focuses on taking the advantages of ML techniques in data process, knowledge representation, classification, NLP, etc., to address challenging issues of software reliability practices. 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 hard-working 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)


  • 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

    • Characterizing and Understanding Software Developer Networks in Security Development
    Song Wang and Nachiappan Nagappan
    ISSRE 2021 (acceptance rate=27% 52/189)
    PDF

  • Continuous Software Bug Prediction
    Song Wang, Junjie Wang, Jaechang Nam, and Nachiappan Nagappan
    ESEM 2021 (acceptance rate=19% 24/124)
    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

  • Characterizing Crowds to Better Optimize Worker Recommendation in Crowdsourced Testing
    Junjie Wang, Song Wang, Jianfeng Chen, Tim Menzies, Qiang Cui, Miao Xie, and Qing Wang.
    IEEE Transaction on Software Engineering 2019
    FSE 2019 Journal First
    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

  • QTEP: Quality-aware Test Case Prioritization
    Song Wang, Jaechang Nam, and Lin Tan
    ESEC/FSE 2017 (acceptance rate=24% 72/295)
    PDF