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), 40 pages. Tiré de https://doi.org/10.1145/3506695
Répertoire des expertises
Li, Heng

Répertoire des expertises
Li, Heng
Répertoire des expertises
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Article de revue (18)
Communication de conférence (9)
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Heng Li (28)
- Articles de revue (18)
- 2022
Article de revue Article de revue 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. Tiré de https://doi.org/10.1109/TSE.2021.3093246Article de revue 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. Tiré de https://doi.org/10.1007/s10664-022-10118-5Article de revue 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. Tiré de https://doi.org/10.1109/TSE.2021.3083715Article de revue Zhang, H., Tang, Y., Lamothe, M., Li, H. & Shang, W. (2022). Studying logging practice in test code. Empirical Software Engineering, 27(4), 45 pages. Tiré de https://doi.org/10.1007/s10664-022-10139-0
- 2021
Article de revue 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), 38 pages. Tiré de https://doi.org/10.1145/3447876Article de revue Gujral, H., Lal, S. & Li, H. (2021). An exploratory semantic analysis of logging questions. Journal of Software: Evolution and Process, 33(7), 35 pages. Tiré de https://doi.org/10.1002/smr.2361Article de revue 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. Tiré de https://doi.org/10.1109/TSE.2020.2970422Article de revue Zhang, H., Wang, S., Li, H., Chen, T.-H.P. & Hassan, A.E. (2021). A study of C/C++ code weaknesses on stack overflow. IEEE Transactions on Software Engineering. Tiré de https://doi.org/10.1109/TSE.2021.3058985Article de revue 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. Tiré de https://doi.org/10.1109/TSE.2021.3131529Article de revue 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. Tiré de https://doi.org/10.1109/TSE.2021.3129688
- 2020
Article de revue 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. Tiré de https://doi.org/10.1007/s10664-020-09822-xArticle de revue 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. Tiré de https://doi.org/10.1109/TSE.2020.3007554Article de revue Li, Y., Jiang, Z.M.J., 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. Tiré de https://doi.org/10.1145/3385187Article de revue 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, 31 pages. Tiré de https://doi.org/10.1007/s10664-020-09866-z
- 2018
Article de revue 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. Tiré de https://doi.org/10.1007/s10664-018-9595-8
- 2017
Article de revue 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. Tiré de https://doi.org/10.1007/s10664-016-9467-zArticle de revue 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. Tiré de https://doi.org/10.1007/s10664-016-9456-2
- 2022
- Communications de conférence (9)
- 2022
Communication de conférence Majidi, F., Openja, M., Khomh, F. & Li, H. (2022). An Empirical Study on the Usage of Automated Machine Learning Tools. Communication présentée à IEEE International Conference on Software Maintenance and Evolution (ICSME 2022), Limassol, Cyprus (p. 59-70). Tiré de https://doi.org/10.1109/ICSME55016.2022.00014Communication de conférence Ding, Z., Li, H. & Shang, W. (2022). LoGenText: Automatically generating logging texts using neural machine translation. Communication présentée à IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER 2022), Honolulu, HI, USA (p. 349-360). Tiré de https://doi.org/10.1109/SANER53432.2022.00051Communication de conférence Hassan, S., Li, H. & Hassan, A.E. (2022). On the importance of performing app analysis within peer groups. Communication présentée à IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER 2022), Honolulu, HI, USA (p. 890-901). Tiré de https://doi.org/10.1109/SANER53432.2022.00107Communication de conférence Openja, M., Majidi, F., Khomh, F., Chembakottu, B. & Li, H. (2022). Studying the Practices of Deploying Machine Learning Projects on Docker. Communication présentée à 26th ACM International Conference on Evaluation and Assessment in Software Engineering (EASE 2022), Gothenburg, Sweden (p. 190-200). Tiré de https://doi.org/10.1145/3530019.3530039
- 2021
Communication de conférence Li, Z., Li, H., Chen, T.-H.P. & Shang, W. (2021). DeepLV: Suggesting log levels using ordinal based neural networks. Communication présentée à 43rd International Conference on Software Engineering (ICSE 2021) (12 pages). Tiré de https://conf.researchr.org/details/icse-2021/icse-2021-papers/98/DeepLV-Suggesting-Log-Levels-Using-Ordinal-Based-Neural-NetworksCommunication de conférence El aoun, M.R., Li, H., Khomh, F. & Openja, M. (2021). Understanding Quantum Software Engineering Challenges An Empirical Study on Stack Exchange Forums and GitHub Issues. Communication présentée à IEEE International Conference on Software Maintenance and Evolution (ICSME 2021), Luxembourg, Netherlands (p. 343-354). Tiré de https://doi.org/10.1109/ICSME52107.2021.00037
- 2019
Communication de conférence Shariff, S.M., Li, H., Bezemer, C.-P., Hassan, A.E., Nguyen, T.H.D. & Flora, P. (2019). Improving the testing efficiency of selenium-based load tests. Communication présentée à 14th IEEE/ACM International Workshop on Automation of Software Test (AST 2019), Montréal, Québec. Tiré de https://doi.org/10.1109/AST.2019.00008
- 2018
Communication de conférence Li, H., Chen, T.-H.P., Hassan, A.E., Nasser, M. & Flora, P. (2018). Adopting Autonomic Computing Capabilities in Existing Large-Scale Systems: An Industrial Experience Report. Communication présentée à 40th International Conference on Software Engineering (ICSE-SEIP 2018), Gothenburg, Sweden (10 pages). Tiré de https://doi.org/10.1145/3183519.3183544Communication de conférence Li, H. & Zhang, Z. (2018). Predicting the receivers of football passes. Communication présentée à Machine Learning and Data Mining for Sports Analytics (MLSA 2018), Dublin, Ireland (p. 167-177). Tiré de https://doi.org/10.1007/978-3-030-17274-9_15
- 2022
- Thèses (1)
- 2018
Thèse Li, H. (2018). Mining development knowledge to understand and support software logging practices (Thèse de doctorat). Tiré de http://hdl.handle.net/1974/25854
- 2018