Song Wang

Assistant Professor

Research Interests
Bio

Song Wang received his Ph.D. in Computer Engineering from the University of Waterloo in Dec. 2018, MS degree from Chinese Academy of Sciences (China) in Jun. 2014. Before that, he got both his BE degree in Software Engineering and BHRM degree in Human Resource Management from Sichuan University (China) in Jun. 2011. He works at the intersection of Software Engineering and Machine Learning. More specifically, his 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. His research interests include software engineering, software reliability, program analysis, software testing, and machine learning. The tools and techniques developed in his research have already helped detect hundreds of true bugs in open source and commercial projects.

He had also worked with various industry companies, either as a research intern at Microsoft Research or as a developer intern at Morgan Stanley Capital International, Yahoo, and Baidu.

Selected Publications
  • J.Wang, S.Wang, J.Chen, T.Menzies, Q.Cui, M.Xie, and Q.Wang. “Characterizing Crowds to Better Optimize Worker Recommendation in Crowdsourced Testing.” IEEE Transaction on Software Engineering (TSE) 2019.
  • S.Wang, C.Bansal, N.Nagappan, and A.Philip. “Leveraging Change Intents for Characterizing and Identifying Large-Review-Effort Changes.” PROMISE 2019.
  • S.Wang, T.Liu, J.Nam, and L.Tan. “Deep Semantic Feature Learning for Software Defect Prediction.” IEEE Transaction on Software Engineering (TSE) 2018.
  • S.Wang, J.Nam, and L.Tan. “QTEP: Quality-aware Test Case Prioritization.” ESEC/FSE 2017.
  • S.Wang, T.Liu, and L.Tan. “Automatically Learning Semantic Features for Defect Prediction.” ICSE 2016.
  • S.Wang, D.Chollak, D.Attias, and L.Tan. “Bugram: Bug Detection with N-gram Language Models.” ASE 2016.
  • J.Wang, S.Wang, Q.Cui, and Q.Wang. “Local-based Active Classification of Test Report to Assist Crowdsourced Testing.” ASE 2016.
  • E.Wong, L.Zhang, S.Wang, T.Liu, and L.Tan. “DASE: Document-Assisted Symbolic Execution for Improving Automated Software Testing.” ICSE 2015.
Song Wang
Staff Information
Campus Address
Lassonde Building, room 1012A (LAS1012A)
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