Peter (Tse-Hsun) Chen

Assistant Professor, Department of Computer Science and Software Engineering at Concordia University, Canada.

Email: tsehsun AT
Twitter: @petertsehsun

About Myself

I am actively looking for self-motivated students who are interested in solving real-world software system problems. If you are interested in working with me, please send me your application.

I received my PhD in Computer Science from Queen's University in September 2016. I was a member of the Software Analysis and Intelligence Lab (SAIL) and I was supervised by an amazing supervisor Ahmed E. Hassan. Before that, I received my MSc from Queen's University (under the same supervisor), and BSc in Computer Science from the University of British Columbia. During my PhD, I worked as an embedded researcher in the Performance Engineering department at BlackBerry from January 2013 to December 2016.

Research interests: software engineering; performance engineering; software log analysis; mining software repositories; focusing on using program analysis and data mining techniques to improve system performance and quality.


My research focus is on improving the performance of large-scale database-centric software systems. I am particularly interested in applying Program Analysis and Data Mining techniques to help developers improve system performance. I have developed bug detection tools for detecting database-related performance anti-patterns using both static (ICSE 2014) and dynamic analysis (TSE 2016). The tools that I developed are integrated into industrial practice and our experience reports are published at the premier conference in the Software Engineering community (ICSE-SEIP 2016 and ICSE-SEIP 2017).

I am currently exploring how to help developers utilize different abstraction frameworks (e.g., database or big data abstraction frameworks). These abstraction frameworks bridge different programming paradigms by providing high-level APIs to developers. However, such abstraction can also lead to various Software Engineering challenges. In my recent FSE 2016 paper, I propose a light-weight approach to optimize the configuration of application-level caches in database abstraction frameworks by leveraging runtime data.

I also have research experiences and interests in many areas of Software Engineering and Data Mining, including, but not limited to, text analysis (MSR 2012, JSS 2016, and EMSE 2016), mining software repositories (MSR 2014 and MSR 2016-1), and empirical software engineering (MSR 2016-2).


   Refereed Journal Papers

   Refereed Conference Papers

   Refereed Short Papers


Selected Projects