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Heng LiDépartement de génie informatique et génie logiciel
Voir sa fiche dans le Répertoire des Expertises de Polytechnique Montréal
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Aghili, R., Li, H., & Khomh, F. (juin 2025). Protecting Privacy in Software Logs: What Should Be Anonymized? [Communication écrite]. ACM International Conference on the Foundations of Software Engineering (FSE 2025), Trondheim, Norway. Publié dans Proceedings of the ACM on Software Engineering, 2(FSE). Lien externe
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. Lien externe
Aghili, R., Qin, Q., Li, H., & Khomh, F. (octobre 2024). Understanding Web Application Workloads and Their Applications: Systematic Literature Review and Characterization [Communication écrite]. IEEE International Conference on Software Maintenance and Evolution (ICSME 2024), Flagstaff, AZ, USA. Lien externe
Aghili, R., Li, H., & Khomh, F. (2023). Studying the characteristics of AIOps projects on GitHub. Empirical Software Engineering, 28(6), 143 (49 pages). Lien externe
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). Lien externe
Caumartin, G., Qin, Q., Chatragadda, S., Panjrolia, J., Li, H., & Elias Costa, D. (mars 2025). Exploring the Potential of Llama Models in Automated Code Refinement: A Replication Study [Communication écrite]. IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER 2025), Montreal, QC, Canada. Lien externe
Chen, J., Ding, Z., Tang, Y., Sayagh, M., Li, H., Adams, B., & Shang, W. (décembre 2023). IoPV : on inconsistent option performance variations [Communication écrite]. 2023 ESEC/FSE Conferences, San Francisco, CA, USA (13 pages). Lien externe
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). Lien externe
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). Lien externe
Dai, H., Tang, Y., Li, H., & Shang, W. PILAR: Studying and Mitigating the Influence of Configurations on Log Parsing [Communication écrite]. 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE 2023), Melbourne, Australia. Lien externe
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. Lien externe
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. Lien externe
Ding, Z., Li, H., & Shang, W. (mars 2022). LoGenText: Automatically generating logging texts using neural machine translation [Communication écrite]. IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER 2022), Honolulu, HI, USA. Lien externe
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. Lien externe
El aoun, M. R., Li, H., Khomh, F., & Openja, M. (septembre 2021). Understanding Quantum Software Engineering Challenges An Empirical Study on Stack Exchange Forums and GitHub Issues [Communication écrite]. IEEE International Conference on Software Maintenance and Evolution (ICSME 2021), Luxembourg, Netherlands. Lien externe
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). Lien externe
Foalem, P. L., Khomh, F., & Li, H. (2024). Studying logging practice in machine learning-based applications. Information and Software Technology, 170, 107450 (17 pages). Lien externe
Ghari, S., Fokaefs, M., & Li, H. (juin 2025). SparkPerf: A Benchmarking Framework for Evaluating the Performance of Spark Data Analytics Projects [Communication écrite]. IEEE Cloud Summit 2025, Washington, DC, USA. Lien externe
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). Lien externe
Ghadesi, A., Li, H., & Lamothe, M. (2023). What Causes Exceptions in Machine Learning Applications? Mining Machine Learning-Related Stack Traces on Stack Overflow [Ensemble de données]. Lien externe
Gujral, H., Lal, S., & Li, H. (2021). An exploratory semantic analysis of logging questions. Journal of Software: Evolution and Process, 33(7), 35 pages. Lien externe
Huang, S.-W., Wu, X., & Li, H. (juin 2025). LogLSHD: Fast Log Parsing with Locality-Sensitive Hashing and Dynamic Time Warping [Communication écrite]. 21st International Conference on Predictive Models and Data Analytics in Software Engineering (PROMISE 2025)), Trondheim, Norway. Lien externe
Hassan, S., Li, H., & Hassan, A. E. (mars 2022). On the importance of performing app analysis within peer groups [Communication écrite]. IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER 2022), Honolulu, HI, USA. Lien externe
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. Lien externe
Jin, B., Li, H., Nie, P., & Zou, Y. (avril 2026). Energy-Efficient Software Development: A Multi-dimensional Empirical Analysis of Stack Overflow . Dans IEEE/ACM International Conference on Software Engineering (ICSE 2026), Rio de Janeiro, Brazil. Lien externe
Liu, Y., Foundjem, A. T., Khomh, F., & Li, H. (2025). Adversarial attack classification and robustness testing for large language models for code. Empirical Software Engineering, 30(5). Lien externe
Liao, L., Eismann, S., Li, H., Bezemer, C.-P., Costa, D. E., van Hoorn, A., & Shang, W. (avril 2025). Early Detection of Performance Regressions by Bridging Local Performance Data and Architectural Models [Communication écrite]. 47th International Conference on Software Engineering (ICSE 2025), Ottawa, ON, Canada. Lien externe
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, -. Lien externe
Liao, L., Li, H., Shang, W., Sporea, C., Toma, A., & Sajedi, S. (décembre 2023). Adapting Performance Analytic Techniques in a Real-World Database-Centric System: An Industrial Experience Report [Communication écrite]. 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. Lien externe
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. Lien externe
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. Lien externe
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. Lien externe
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 [Ensemble de données]. Lien externe
Li, Z., Li, H., Chen, T.-H. P., & Shang, W. (mai 2021). DeepLV: Suggesting log levels using ordinal based neural networks [Communication écrite]. 43rd International Conference on Software Engineering (ICSE 2021) (12 pages). Lien externe
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. Lien externe
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. Lien externe
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. Lien externe
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. Lien externe
Liao, L., Chen, J., Li, H., Zeng, Y., Shang, W., Sporea, C., Toma, A., & Sajedi, S. (2021). TSE2021 Replication Package [Ensemble de données]. Lien externe
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. Lien externe
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. Lien externe
Li, H., Chen, T.-H. P., Hassan, A. E., Nasser, M., & Flora, P. (mai 2018). Adopting Autonomic Computing Capabilities in Existing Large-Scale Systems: An Industrial Experience Report [Communication écrite]. 40th International Conference on Software Engineering (ICSE-SEIP 2018), Gothenburg, Sweden (10 pages). Lien externe
Li, H. (2018). Mining development knowledge to understand and support software logging practices [Thèse de doctorat]. Lien externe
Li, H., & Zhang, Z. (septembre 2018). Predicting the receivers of football passes [Communication écrite]. Machine Learning and Data Mining for Sports Analytics (MLSA 2018), Dublin, Ireland. Lien externe
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. Lien externe
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. Lien externe
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. Lien externe
Majidi, F., Khomh, F., Li, H., & Nikanjam, A. (2025). An efficient model maintenance approach for MLOps. Empirical Software Engineering, 31(1), 48 pages. Lien externe
Majidi, F., Openja, M., Khomh, F., & Li, H. (octobre 2022). An Empirical Study on the Usage of Automated Machine Learning Tools [Communication écrite]. IEEE International Conference on Software Maintenance and Evolution (ICSME 2022), Limassol, Cyprus. Lien externe
Noei, S., Li, H., & Zhou, Y. (2025). An empirical study on release-wise refactoring patterns. [Autre type de communication de conférence]. Proceedings of the ACM on Software Engineering, 2(FSE), 403-424. Présentée à ACM SIGSOFT International Conference on Software Testing and Analysis (ISSTA), Trondheim, Norway. Disponible
Njoku, A. O., Li, H., & Khomh, F. (mai 2025). Kernel-Level Event-Based Performance Anomaly Detection in Software Systems under Varying Load Conditions [Communication écrite]. 16th International Conference on Performance Engineering (ICPE 2025), Toronto, ON, Canada. Lien externe
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. Lien externe
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. Lien externe
Openja, M., Majidi, F., Khomh, F., Chembakottu, B., & Li, H. (juin 2022). Studying the Practices of Deploying Machine Learning Projects on Docker [Communication écrite]. 26th ACM International Conference on Evaluation and Assessment in Software Engineering (EASE 2022), Gothenburg, Sweden. Lien externe
Qin, Q., Djian, B. P. P., Merlo, E., Li, H., & Gambs, S. (2025). Representation-based fairness evaluation and bias correction robustness assessment in neural networks. Information and Software Technology, 107876 (21 pages). Lien externe
Qin, Q., Aghili, R., Li, H., & Merlo, E. (mars 2025). Preprocessing is All You Need: Boosting the Performance of Log Parsers with a General Preprocessing Framework [Communication écrite]. IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER 2025), Montreal, QC, Canada. Lien externe
Qin, Q., Li, H., Merlo, E., & Lamothe, M. (2025). Automated, Unsupervised, and Auto-parameterized Inference of Data Patterns and Anomaly Detection [Ensemble de données]. Lien externe
Qin, Q., Li, H., Merlo, E., & Lamothe, M. (avril 2025). Automated, Unsupervised, and Auto-Parameterized Inference of Data Patterns and Anomaly Detection [Communication écrite]. 47th International Conference on Software Engineering (ICSE 2025), Ottawa, ON, Canada. Lien externe
Shahedi, K., Li, H., Lamothe, M., & Khomh, F. (2025). Tracing Optimization for Performance Modeling and Regression Detection. ACM Transactions on Software Engineering and Methodology. Lien externe
Shahedi, K., Lamothe, M., Khomh, F., & Li, H. (avril 2025). JPerfEvo: A Tool for Tracking Method-Level Performance Changes in Java Projects [Communication écrite]. 22nd International Conference on Mining Software Repositories (MSR 2025), Ottawa, ON, Canada. Lien externe
Shariff, S. M., Li, H., Bezemer, C.-P., Hassan, A. E., Nguyen, T. H. D., & Flora, P. (mai 2019). Improving the testing efficiency of selenium-based load tests [Communication écrite]. 14th IEEE/ACM International Workshop on Automation of Software Test (AST 2019), Montréal, Québec. Lien externe
Traini, L., & Li, H. (mai 2024). Workshop on Challenges in Performance Methods for Software Development (WOSP-C) [Résumé]. 15th ACM/SPEC International Conference on Performance Engineering, London, United Kingdom. Lien externe
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. Lien externe
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). Lien externe
Wu, X., Li, H., Yoshioka, N., Washizaki, H., & Khomh, F. (mars 2024). Refining GPT-3 Embeddings with a Siamese Structure for Technical Post Duplicate Detection [Communication écrite]. 31st IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER 2024), Rovaniemi, Finland. Lien externe
Wu, X., Li, H., & Khomh, F. (2023). Supplimental Materials - Truncated Spirit and Thunderbird datasets [Ensemble de données]. Lien externe
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). Lien externe
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). Lien externe
Yahmed, A. H., Allah Abbassi, A., Nikanjam, A., Li, H., & Khomh, F. (octobre 2023). Deploying deep reinforcement learning systems: a taxonomy of challenges [Communication écrite]. IEEE International Conference on Software Maintenance and Evolution (ICSME 2023), Bogota, Colombia. Lien externe
Yousefifeshki, F., Li, H., & Khomh, F. (2023). Studying the challenges of developing hardware description language programs. Information and Software Technology, 159, 16 pages. Lien externe
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. Lien externe
Zishuo, D., Yiming, T., Yang, L., Li, H., & Weiyi, S. (mai 2023). On the Temporal Relations between Logging and Code [Communication écrite]. 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE 2023), Melbourne, Australia. Lien externe
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. Lien externe
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). Lien externe
