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Heng Li (28)

  • 2022 (9)
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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 (8)
    • 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
    • Communication 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
  • 2020 (4)
    • 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
  • 2019 (1)
    • 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 (4)
    • 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
    • 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
    • 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
    • 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 (2)
    • 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