Department of Computer Engineering and Software Engineering
Research interests and affiliations
- Software engineering
- Software monitoring & observability
- Engineering and operations of intelligent (AI-powered) software systems
- Software performance/sustainability
- Quantum software engineering
- DevOps & AIOps
- Software logging & log analytics
- Mining software repositories
- Software maintenance and evolution
- Machine learning & data mining
- 2705 Software and development
- 2706 Software engineering
- 2720 Computer systems software
Publications
Biography
Heng Li leads the Measurement, Observation, and Optimization of Software and its Evolution (MOOSE) lab at Polytechnique Montréal. The mission of MOOSE is to invent intelligent and novel solutions for the monitoring/observability and operations of large, complex, and evolving software systems, including traditional systems and emerging systems (e.g., AI-based and quantum systems). Heng Li received his Ph.D. in Computing from Queen’s University in 2018. At Queen’s University, he worked as a PhD student and later as a Postdoctoral Fellow with the NSERC/RIM Industrial Research Chair in Software Engineering in the Software Analysis and Intelligence Lab (SAIL). He also obtained his B.Eng. from Sun Yat-sen University (China) and M.Sc. from Fudan University (China). He has several years of experience in the industry (Synopsys, BlackBerry) doing software development and research. To learn more about his research, please visit the MOOSE lab website.
Teaching
- LOG8371E – Software Quality Engineering
- LOG6309E – Intelligent DevOps of Large-Scale Software Systems
- LOG8490 – Quantum Software Engineering
Education
- B.Eng. in Electronics Engineering, Sun Yat-sen University, Guangzhou, China
- M.Sc. in Electronics Engineering, Fudan University, Shanghai, China
- Ph.D. in Computing, Queen’s University, Kingston, Canada
- Postdoctoral Fellow, Queen’s University, Kingston, Canada
Supervision at Polytechnique
COMPLETED
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Ph.D. Thesis (3)
- Wu, X. (2024). Representation Learning for Enhanced Software Analytics [Ph.D. thesis, Polytechnique Montréal].
- Ghari, S. (2023). Benchmarking Framework and Performance Modeling for Evaluating the Performance of Spark-Based Data Science Projects [Ph.D. thesis, Polytechnique Montréal].
- Rasolroveicy, M. (2022). Toward More Performant and Efficient Decentralized Applications on Blockchain Technologies [Ph.D. thesis, Polytechnique Montréal].
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Master's Thesis (5)
- Chembakottu, B. (2023). Understanding User Perception of Mobile App Quality: A Large-Scale Multi-Dimension Study of the Reviews and Ratings of Android Sports Apps [Master's thesis, Polytechnique Montréal].
- Ghadesi, A. (2023). An Empirical Study of the Causes and Symptoms of Machine Learning Exceptions [Master's thesis, Polytechnique Montréal].
- Saint-Cyr, B. (2023). Traçage multi-niveaux d'orchestrateur d'application conteneurisée [Master's thesis, Polytechnique Montréal].
- El Aoun, M. R. (2022). Empirical Studies of Quantum Programming Issues [Master's thesis, Polytechnique Montréal].
- Yousefifeshki, F. (2022). Studying the Practices and Challenges of Developing Hardware Description Language Programs [Master's thesis, Polytechnique Montréal].
- Chembakottu, B. (2023). Understanding User Perception of Mobile App Quality: A Large-Scale Multi-Dimension Study of the Reviews and Ratings of Android Sports Apps [Master's thesis, Polytechnique Montréal].
