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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
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., 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
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
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
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
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., 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
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
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). 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
