Registered user since Sat 30 Jan 2021
Heng Li is an Assistant Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montreal, Montreal, Canada, where he leads the Measurement, Observation, and Optimization of Software and its Evolution (MOOSE) lab. He obtained his Ph.D. from the School of Computing, Queen’s University (Canada), M.Sc. from Fudan University (China), and B.Eng. from Sun Yat-sen University (China). He also worked in the industry for multiple years as a software engineer at Synopsys and as a software performance engineer at BlackBerry. His research interests lie within Software Engineering, in particular, software monitoring and observability, intelligent operations of software systems, software log mining, software performance engineering, and mining software repositories.
Contributions
2025
2024
ICSE
- Author of Studying the Characteristics of AIOps Projects on GitHub within the Journal-first Papers-track
- Committee Member in Technical Briefings within the Technical Briefings-track
- Author of An Empirical Study of Refactoring Rhythms and Tactics in the Software Development Process within the Journal-first Papers-track
- Author of On the Effectiveness of Log Representation for Log-based Anomaly Detection within the Journal-first Papers-track
- Committee Member in Joint Track on Software Engineering Education and Training within the Software Engineering Education and Training-track
SANER
2023
Mining Software Repositories
ESEC/FSE
- Author of Adapting Performance Analytic Techniques in a Real-World Database-Centric System: An Industrial Experience Report within the Industry Papers-track
- Author of IoPV: On Inconsistent Option Performance Variations within the Research Papers-track
- Committee Member in Program Committee within the Student Research Competition-track
- Committee Member in Program Committee within the Artifacts-track
EASE
ICSE
- Author of On the Temporal Relations between Logging and Code within the Technical Track-track
- Author of Towards Learning Generalizable Code Embeddings using Task-agnostic Graph Convolutional Networks within the Journal-First Papers-track
- Author of An Empirical Study of the Impact of Hyperparameter Tuning and Model Optimization on the Performance Properties of Deep Neural Networks within the Journal-First Papers-track
- Author of PILAR: Studying and Mitigating the Influence of Configurations on Log Parsing within the Technical Track-track
2022
ESEC/FSE
- Author of Can pre-trained code embeddings improve model performance? Revisiting the use of code embeddings in software engineering tasks within the Journal First-track
- Author of Studying logging practice in test code within the Journal First-track
- Author of Locating Performance Regression Root Causes in the Field Operations of Web-based Systems: An Experience Report within the Journal First-track
Mining Software Repositories
2021
ASE
ESEC/FSE
- Author of A Qualitative Study of the Benefits and Costs of Logging from Developers' Perspectives: A Journal First Presentation Proposal within the Journal First-track
- Author of Predicting Node Failures in an Ultra-large-scale Cloud Computing Platform: an AIOps Solution: A Journal First Presentation Proposal within the Journal First-track
- Committee Member in Program Committee within the Artifacts-track
ICSE
- Author of DeepLV: Suggesting Log Levels Using Ordinal Based Neural Networks within the Technical Track-track
- Author of Using black-box performance models to detect performance regressions under varying workloads: an empirical study within the Journal-First Papers-track
- Author of Logram: Efficient Log Parsing Using n-Gram Dictionaries within the Journal-First Papers-track