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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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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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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., 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
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
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
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
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
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
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
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
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
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
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
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
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
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
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., & 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
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
Li, H. (2018). Mining development knowledge to understand and support software logging practices [Thèse de doctorat]. Lien externe
Wu, X., Li, H., & Khomh, F. (2023). Supplimental Materials - Truncated Spirit and Thunderbird datasets [Ensemble de données]. 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
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
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
