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Article de revue (18) Communication de conférence (9) Livre Chapitre de livre Brevet Rapport Thèse (1) Ensemble de données Ressource pédagogique Image Enregistrement audio Enregistrement vidéo Autre

Heng Li (28)

  • Articles de revue (18)
    • 2022
      • Article de revue
        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
      • 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.2021.3093246
      • Article 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/s10664-022-10118-5
      • Article 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.2021.3083715
      • Article 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/10.1145/3447876
      • Article 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.2361
      • Article 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.2020.2970422
      • Article 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.2021.3058985
      • Article 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.2021.3131529
      • Article 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-x
      • Article 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.2020.3007554
      • Article 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/10.1145/3385187
      • Article 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-z
      • Article 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
  • 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/ICSME55016.2022.00014
      • Communication 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/SANER53432.2022.00051
      • Communication 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/SANER53432.2022.00107
      • Communication 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
    • 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/10.1145/3183519.3183544
      • Communication 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
  • 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