Documents dont l'auteur est "Li, Heng"

Heng LiDépartement de génie informatique et génie logicielVoir sa fiche dans le Répertoire des Expertises de Polytechnique Montréal

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A

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

B

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

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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

D

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

E

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

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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

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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

H

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

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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

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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

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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

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

O

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

S

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

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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

W

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

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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

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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

Z

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