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Heng LiDepartment of Computer Engineering and Software Engineering
Profile in the Directory of Experts of Polytechnique Montréal
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Aghili, R., Li, H., & Khomh, F. (2025, June). Protecting Privacy in Software Logs: What Should Be Anonymized? [Paper]. ACM International Conference on the Foundations of Software Engineering (FSE 2025), Trondheim, Norway. Published in Proceedings of the ACM on Software Engineering, 2(FSE). External link
Aghili, R., Li, H., & Khomh, F. (2025). Protecting Privacy in Software Logs: What Should Be Anonymized? Proceedings of the ACM on software engineering., 2(FSE), 1317-1338. External link
Aghili, R., Qin, Q., Li, H., & Khomh, F. (2024, October). Understanding Web Application Workloads and Their Applications: Systematic Literature Review and Characterization [Paper]. IEEE International Conference on Software Maintenance and Evolution (ICSME 2024), Flagstaff, AZ, USA. External link
Aghili, R., Li, H., & Khomh, F. (2023). Studying the characteristics of AIOps projects on GitHub. Empirical Software Engineering, 28(6), 143 (49 pages). External link
Batoun, M. A., Sayagh, M., Aghili, R., Ouni, A., & Li, H. (2024). A literature review and existing challenges on software logging practices: From the creation to the analysis of software logs. Empirical Software Engineering, 29, 103 (61 pages). External link
Caumartin, G., Qin, Q., Chatragadda, S., Panjrolia, J., Li, H., & Elias Costa, D. (2025, March). Exploring the Potential of Llama Models in Automated Code Refinement: A Replication Study [Paper]. IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER 2025), Montreal, QC, Canada. External link
Chen, J., Ding, Z., Tang, Y., Sayagh, M., Li, H., Adams, B., & Shang, W. (2023, December). IoPV : on inconsistent option performance variations [Paper]. 2023 ESEC/FSE Conferences, San Francisco, CA, USA (13 pages). External link
Chembakottu, B., Li, H., & Khomh, F. (2023). A large-scale exploratory study of android sports apps in the google play store. Information and Software Technology, 164, 107321 (18 pages). External link
Ding, Z., Tang, Y., Cheng, X., Li, H., & Shang, W. (2024). LoGenText-Plus : Improving Neural Machine Translation Based Logging Texts Generation with Syntactic Templates. ACM Transactions on Software Engineering and Methodology, 33(2), 38 (45 pages). External link
Dai, H., Tang, Y., Li, H., & Shang, W. PILAR: Studying and Mitigating the Influence of Configurations on Log Parsing [Paper]. 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE 2023), Melbourne, Australia. External link
Ding, Z., Li, H., Shang, W., & Chen, T.-H. P. (2023). Towards Learning Generalizable Code Embeddings Using Task-agnostic Graph Convolutional Networks. ACM Transactions on Software Engineering and Methodology, 32(2), 1-43. External link
Ding, Z., Li, H., Shang, W., & Chen, T.-H. P. (2022). Can pre-trained code embeddings improve model performance? Revisiting the use of code embeddings in software engineering tasks. Empirical Software Engineering, 27(3), 38 pages. External link
Ding, Z., Li, H., & Shang, W. (2022, March). LoGenText: Automatically generating logging texts using neural machine translation [Paper]. IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER 2022), Honolulu, HI, USA. External link
Dai, H., Li, H., Chen, C.-S., Shang, W., & Chen, T.-H. (2020). Logram: Efficient log parsing using n-gram dictionaries. IEEE Transactions on Software Engineering, 14 pages. External link
El aoun, M. R., Li, H., Khomh, F., & Openja, M. (2021, September). Understanding Quantum Software Engineering Challenges An Empirical Study on Stack Exchange Forums and GitHub Issues [Paper]. IEEE International Conference on Software Maintenance and Evolution (ICSME 2021), Luxembourg, Netherlands. External link
Foalem, P. L., Da Silva, L. M. P., Khomh, F., Li, H., & Merlo, E. (2025). Logging requirement for continuous auditing of responsible machine learning-based applications. Empirical Software Engineering, 30(3), 97 (37 pages). External link
Foalem, P. L., Khomh, F., & Li, H. (2024). Studying logging practice in machine learning-based applications. Information and Software Technology, 170, 107450 (17 pages). External link
Ghadesi, A., Lamothe, M., & Li, H. (2024). What causes exceptions in machine learning applications? Mining machine learning-related stack traces on Stack Overflow. Empirical Software Engineering, 29, 107 (37 pages). External link
Ghadesi, A., Li, H., & Lamothe, M. (2023). What Causes Exceptions in Machine Learning Applications? Mining Machine Learning-Related Stack Traces on Stack Overflow [Dataset]. External link
Gujral, H., Lal, S., & Li, H. (2021). An exploratory semantic analysis of logging questions. Journal of Software: Evolution and Process, 33(7), 35 pages. External link
Huang, S.-W., Wu, X., & Li, H. (2025, June). LogLSHD: Fast Log Parsing with Locality-Sensitive Hashing and Dynamic Time Warping [Paper]. 21st International Conference on Predictive Models and Data Analytics in Software Engineering (PROMISE 2025)), Trondheim, Norway. External link
Hassan, S., Li, H., & Hassan, A. E. (2022, March). On the importance of performing app analysis within peer groups [Paper]. IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER 2022), Honolulu, HI, USA. External link
Jin, B., Li, H., & Zou, Y. (2025). Impact of extensions on browser performance: An empirical study on google chrome. Empirical Software Engineering, 30(3), 41 pages. External link
Liao, L., Eismann, S., Li, H., Bezemer, C.-P., Costa, D. E., van Hoorn, A., & Shang, W. (2025, April). Early Detection of Performance Regressions by Bridging Local Performance Data and Architectural Models [Paper]. 47th International Conference on Software Engineering (ICSE 2025), Ottawa, ON, Canada. External link
Lyu, Y., Li, H., Jiang, Z. M., & Hassan, A. E. (2024). On the Model Update Strategies for Supervised Learning in AIOps Solutions. ACM Transactions on Software Engineering and Methodology, -. External link
Liao, L., Li, H., Shang, W., Sporea, C., Toma, A., & Sajedi, S. (2023, December). Adapting Performance Analytic Techniques in a Real-World Database-Centric System: An Industrial Experience Report [Paper]. 31st ACM Joint Meeting of the European Software Engineering Conference / Symposium on the Foundations-of-Software-Engineering (ESEC/FSE), San Francisco, CA San Francisco, CA. External link
Lamothe, M., Li, H., & Shang, W. (2022). Assisting Example-based API Misuse Detection via Complementary Artificial Examples. IEEE Transactions on Software Engineering, 48(9), 3410-3422. External link
Liao, L., Li, H., Shang, W., & Ma, L. (2022). An Empirical Study of the Impact of Hyperparameter Tuning and Model Optimization on the Performance Properties of Deep Neural Networks. ACM Transactions on Software Engineering and Methodology, 31(3), 1-40. External link
Locke, S., Li, H., Chen, T.-H., Shang, W., & Liu, W. (2022). LogAssist: Assisting Log Analysis Through Log Summarization. IEEE Transactions on Software Engineering, 48(9), 3227-3241. External link
Liao, L., Chen, J., Li, H., Zeng, Y., Shang, W., Sporea, C., Toma, A., & Sajedi, S. (2021). Replication package - Locating Performance Regression Root Causes in the Field for Web-based Systems [Dataset]. External link
Li, Z., Li, H., Chen, T.-H. P., & Shang, W. (2021, May). DeepLV: Suggesting log levels using ordinal based neural networks [Paper]. 43rd International Conference on Software Engineering (ICSE 2021) (12 pages). External link
Lyu, Y., Li, H., Sayagh, M., Jiang, Z. M., & Hassan, A. E. (2021). An empirical study of the impact of data splitting decisions on the performance of AiOps solutions. ACM Transactions on Software Engineering and Methodology, 30(4), 1-38. External link
Liao, L., Chen, J., Li, H., Zeng, Y., Shang, W., Sporea, C., Toma, A., & Sajedi, S. (2021). Locating Performance Regression Root Causes in the Field Operations of Web-based Systems: An Experience Report. IEEE Transactions on Software Engineering, 22 pages. External link
Li, H., Shang, W., Adams, B., Sayagh, M., & Hassan, A. E. (2021). A qualitative study of the benefits and costs of logging from developers' perspectives. IEEE Transactions on Software Engineering, 47(12), 2858-2873. External link
Li, H., Zhang, H., Wang, S., & Hassan, A. E. (2021). Studying the Practices of Logging Exception Stack Traces in Open-Source Software Projects. IEEE Transactions on Software Engineering, 19 pages. External link
Liao, L., Chen, J., Li, H., Zeng, Y., Shang, W., Sporea, C., Toma, A., & Sajedi, S. (2021). TSE2021 Replication Package [Dataset]. External link
Li, Y., Jiang, Z. M., Li, H., Hassan, A. E., He, C., Huang, R., Zeng, Z., Wang, M., & Chen, P. (2020). Predicting node failures in an ultra-large-scale cloud computing platform: An AIOps solution. ACM Transactions on Software Engineering and Methodology, 29(2), 13:1-13:24-13:1-13:24. External link
Liao, L., Chen, J., Li, H., Zeng, Y., Shang, W., Guo, J., Sporea, C., Toma, A., & Sajedi, S. (2020). Using black-box performance models to detect performance regressions under varying workloads: an empirical study. Empirical Software Engineering, 25(5), 4130-4160. External link
Li, H., Chen, T.-H. P., Hassan, A. E., Nasser, M., & Flora, P. (2018, May). Adopting Autonomic Computing Capabilities in Existing Large-Scale Systems: An Industrial Experience Report [Paper]. 40th International Conference on Software Engineering (ICSE-SEIP 2018), Gothenburg, Sweden (10 pages). External link
Li, H. (2018). Mining development knowledge to understand and support software logging practices [Ph.D. Thesis]. External link
Li, H., & Zhang, Z. (2018, September). Predicting the receivers of football passes [Paper]. Machine Learning and Data Mining for Sports Analytics (MLSA 2018), Dublin, Ireland. External link
Li, H., Chen, T.-H. P., Shang, W., & Hassan, A. E. (2018). Studying software logging using topic models. Empirical Software Engineering, 23(5), 2655-2694. External link
Li, H., Shang, W., Zou, Y., & Hassan, A. E. (2017). Towards just-in-time suggestions for log changes. Empirical Software Engineering, 22(4), 1831-1865. External link
Li, H., Shang, W., & Hassan, A. E. (2017). Which log level should developers choose for a new logging statement? Empirical Software Engineering, 22(4), 1684-1716. External link
Majidi, F., Openja, M., Khomh, F., & Li, H. (2022, October). An Empirical Study on the Usage of Automated Machine Learning Tools [Paper]. IEEE International Conference on Software Maintenance and Evolution (ICSME 2022), Limassol, Cyprus. External link
Noei, S., Li, H., & Zhou, Y. (2025, June). An Empirical Study on Release-Wise Refactoring Patterns [Paper]. ACM International Conference on the Foundations of Software Engineering (FSE 2025), Trondheim, Norway (21 pages). Published in Proceedings of the ACM on Software Engineering, 2(FSE). External link
Njoku, A. O., Li, H., & Khomh, F. (2025, May). Kernel-Level Event-Based Performance Anomaly Detection in Software Systems under Varying Load Conditions [Paper]. 16th International Conference on Performance Engineering (ICPE 2025), Toronto, ON, Canada. External link
Noei, S., Li, H., & Zou, Y. (2024). Detecting Refactoring Commits in Machine Learning Python Projects: A Machine Learning-Based Approach. ACM Transactions on Software Engineering and Methodology, 24 pages. External link
Noei, S., Li, H., Georgiou, S., & Zou, Y. (2023). An Empirical Study of Refactoring Rhythms and Tactics in the Software Development Process. IEEE Transactions on Software Engineering, 49(12), 5103-5119. External link
Openja, M., Majidi, F., Khomh, F., Chembakottu, B., & Li, H. (2022, June). Studying the Practices of Deploying Machine Learning Projects on Docker [Paper]. 26th ACM International Conference on Evaluation and Assessment in Software Engineering (EASE 2022), Gothenburg, Sweden. External link
Qin, Q., Aghili, R., Li, H., & Merlo, E. (2025, March). Preprocessing is All You Need: Boosting the Performance of Log Parsers with a General Preprocessing Framework [Paper]. IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER 2025), Montreal, QC, Canada. External link
Qin, Q., Li, H., Merlo, E., & Lamothe, M. (2025). Automated, Unsupervised, and Auto-parameterized Inference of Data Patterns and Anomaly Detection [Dataset]. External link
Qin, Q., Li, H., Merlo, E., & Lamothe, M. (2025, April). Automated, Unsupervised, and Auto-Parameterized Inference of Data Patterns and Anomaly Detection [Paper]. 47th International Conference on Software Engineering (ICSE 2025), Ottawa, ON, Canada. External link
Shahedi, K., Lamothe, M., Khomh, F., & Li, H. (2025, April). JPerfEvo: A Tool for Tracking Method-Level Performance Changes in Java Projects [Paper]. 22nd International Conference on Mining Software Repositories (MSR 2025), Ottawa, ON, Canada. External link
Shariff, S. M., Li, H., Bezemer, C.-P., Hassan, A. E., Nguyen, T. H. D., & Flora, P. (2019, May). Improving the testing efficiency of selenium-based load tests [Paper]. 14th IEEE/ACM International Workshop on Automation of Software Test (AST 2019), Montréal, Québec. External link
Traini, L., & Li, H. (2024, May). Workshop on Challenges in Performance Methods for Software Development (WOSP-C) [Abstract]. 15th ACM/SPEC International Conference on Performance Engineering, London, United Kingdom. External link
Wu, X., Li, H., & Khomh, F. (2025). What information contributes to log-based anomaly detection? Insights from a configurable transformer-based approach. Automated Software Engineering, 32(2), 29 pages. External link
Wu, X., Laufer, E., Li, H., Khomh, F., Srinivasan, S., & Luo, J. (2024). Characterizing and classifying developer forum posts with their intentions. Empirical Software Engineering, 29(4), 84 (34 pages). External link
Wu, X., Li, H., Yoshioka, N., Washizaki, H., & Khomh, F. (2024, March). Refining GPT-3 Embeddings with a Siamese Structure for Technical Post Duplicate Detection [Paper]. 31st IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER 2024), Rovaniemi, Finland. External link
Wu, X., Li, H., & Khomh, F. (2023). Supplimental Materials - Truncated Spirit and Thunderbird datasets [Dataset]. External link
Wu, X., Li, H., & Khomh, F. (2023). On the effectiveness of log representation for log-based anomaly detection. Empirical Software Engineering, 28(6), 137 (39 pages). External link
Xia, Y., Liao, L., Chen, J., Li, H., & Shang, W. (2024). Reducing the Length of Field-replay Based Load Testing. IEEE Transactions on Software Engineering, 3408079 (17 pages). External link
Yahmed, A. H., Allah Abbassi, A., Nikanjam, A., Li, H., & Khomh, F. (2023, October). Deploying deep reinforcement learning systems: a taxonomy of challenges [Paper]. IEEE International Conference on Software Maintenance and Evolution (ICSME 2023), Bogota, Colombia. External link
Yousefifeshki, F., Li, H., & Khomh, F. (2023). Studying the challenges of developing hardware description language programs. Information and Software Technology, 159, 16 pages. External link
Yao, K., Li, H., Shang, W., & Hassan, A. E. (2020). A study of the performance of general compressors on log files. Empirical Software Engineering, 25(5), 3043-3085. External link
Zishuo, D., Yiming, T., Yang, L., Li, H., & Weiyi, S. (2023, May). On the Temporal Relations between Logging and Code [Paper]. 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE 2023), Melbourne, Australia. External link
Zhang, H., Wang, S., Li, H., Chen, T.-H. P., & Hassan, A. E. (2022). A study of C/C++ code weaknesses on stack overflow. IEEE Transactions on Software Engineering, 48(7), 2359-2375. External link
Zhang, H., Tang, Y., Lamothe, M., Li, H., & Shang, W. (2022). Studying logging practice in test code. Empirical Software Engineering, 27(4), 83 (45 pages). External link
