Publications

2023

  1. Z. Ding, Y. Tang, Y. Li, H. Li, and and Weiyi Shang, “On the Temporal Relations between Logging and Code,” in Proceedings of the 45rd International Conference on Software Engineering (ICSE), 2023.
  2. H. Dai, Y. Tang, H. Li, and W. Shang, “PILAR: Studying and Mitigating the Influence of Configurations on Log Parsing,” in Proceedings of the 45rd International Conference on Software Engineering (ICSE), 2023.

2022

  1. F. Majidi, M. Openja, F. Khomh, and H. Li, “An Empirical Study on the Usage of Automated Machine Learning Tools,” in Proceedings of the 38th IEEE International Conference on Software Maintenance and Evolution (ICSME), 2022.
  2. M. Raed, H. Li, F. Khomh, and L. Tidjon, “Bug Characteristics in Quantum Software Ecosystem,” arXiv preprint arXiv:2204.11965, 2022.
  3. Z. Ding, H. Li, W. Shang, and T.-H. Chen, “Towards Learning Generalizable Code Embeddings using Task-agnostic Graph Convolutional Networks,” ACM Transactions on Software Engineering and Methodology (TOSEM), 2022.
  4. M. Openja, F. Majidi, F. Khomh, B. Chembakottu, and H. Li, “Studying the Practices of Deploying Machine Learning Projects on Docker,” in Proceedings of the 26th International Conference on Evaluation and Assessment in Software Engineering (EASE), 2022.
  5. H. Zhang, Y. Tang, M. Lamothe, H. Li, and W. Shang, “Studying Logging Practice in Test Code,” Empirical Software Engineering (EMSE), vol. 27, no. 4, pp. 1–45, 2022.
  6. Z. Ding, H. Li, W. Shang, and T.-H. Chen, “Can Pre-trained Code Embeddings Improve Model Performance? Revisiting the Use of Code Embeddings in Software Engineering Tasks,” Empirical Software Engineering (EMSE), vol. 27, no. 3, pp. 1–38, 2022.
  7. Z. Ding, H. Li, and W. Shang, “LoGenText: Automatically Generating Logging Texts Using Neural Machine Translation,” in Proceedings of the 9th IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), 2022.
  8. S. Hassan, H. Li, and A. E. Hassan, “On the Importance of Performing App Analysis Within Peer Groups,” in Proceedings of the 9th IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), 2022.
  9. L. Liao, H. Li, W. Shang, and L. Ma, “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 (TOSEM), vol. 31, no. 3, pp. 1–40, 2022.

2021

  1. L. Liao, J. Chen, H. Li, Y. Zeng, W. Shang, C. Sporea, A. Toma, and S. Sajedi, “Locating Performance Regression Root Causes in the Field Operations of Web-based Systems: An Experience Report,” IEEE Transactions on Software Engineering (TSE), 2021.
  2. H. Li, H. Zhang, S. Wang, and A. E. Hassan, “Studying the Practices of Logging Exception Stack Traces in Open-Source Software Projects,” IEEE Transactions on Software Engineering, 2021.
  3. M. Raed, H. Li, F. Khomh, and M. Openja, “Understanding Quantum Software Engineering Challenges: An Empirical Study on Stack Exchange Forums and GitHub Issues,” in Proceedings of the 37th IEEE International Conference on Software Maintenance and Evolution (ICSME), 2021.
  4. M. Lamothe, H. Li, and W. Shang, “Assisting Example-based API Misuse Detection via Complementary Artificial Examples,” IEEE Transactions on Software Engineering (TSE), 2021.
  5. S. Locke, H. Li, T.-H. P. Chen, W. Shang, and W. Liu, “LogAssist: Assisting Log Analysis Through Log Summarization,” IEEE Transactions on Software Engineering (TSE), 2021.
  6. H. Gujral, S. Lal, and H. Li, “An Exploratory Semantic Analysis of Logging Questions,” Journal of Software: Evolution and Process (JSME), vol. 33, no. 7, p. e2361, 2021.
  7. H. Zhang, S. Wang, H. Li, T.-H. P. Chen, and A. E. Hassan, “A Study of C/C++ Code Weaknesses on Stack Overflow,” IEEE Transactions on Software Engineering (TSE), 2021.
  8. Y. Lyu, H. Li, M. Sayagh, Z. M. Jiang, and A. E. Hassan, “An Empirical Study of the Impact of Data Splitting Decisions on the Performance of AIOps Solutions,” ACM Transactions on Software Engineering and Methodology (TOSEM), vol. 30, no. 4, pp. 1–38, 2021.
  9. Z. Li, H. Li, T.-H. P. Chen, and W. Shang, “DeepLV: Suggesting Log Levels Using Ordinal Based Neural Networks,” in Proceedings of the 43rd International Conference on Software Engineering (ICSE), 2021.

2020

  1. L. Liao, J. Chen, H. Li, Y. Zeng, W. Shang, J. Guo, C. Sporea, A. Toma, and S. Sajedi, “Using Black-Box Performance Models to Detect Performance Regressions under Varying Workloads: An Empirical Study,” Empirical Software Engineering (EMSE), vol. 25, no. 5, pp. 4130–4160, 2020.
  2. H. Dai, H. Li, C.-S. Chen, W. Shang, and T.-H. Chen, “Logram: Efficient Log Parsing Using n-Gram Dictionaries,” IEEE Transactions on Software Engineering (TSE), vol. 48, no. 3, pp. 879–892, 2020.
  3. K. Yao, H. Li, W. Shang, and A. E. Hassan, “A Study of the Performance of General Compressors on Log Files,” Empirical Software Engineering (EMSE), vol. 25, no. 5, pp. 3043–3085, 2020.
  4. Y. Li, Z. M. Jiang, H. Li, A. E. Hassan, C. He, R. Huang, Z. Zeng, M. Wang, and P. Chen, “Predicting Node Failures in an Ultra-Large-Scale Cloud Computing Platform: An AIOps Solution,” ACM Transactions on Software Engineering and Methodology (TOSEM), vol. 29, no. 2, pp. 13:1–13:24, 2020.
  5. H. Li, W. Shang, B. Adams, M. Sayagh, and A. Hassan, “A Qualitative Study of the Benefits and Costs of Logging from Developers’ Perspectives,” IEEE Transactions on Software Engineering (TSE), vol. 47, no. 12, pp. 2858–2873, 2020.

2019

  1. S. M. Shariff, H. Li, C.-P. Bezemer, A. E. Hassan, T. H. D. Nguyen, and P. Flora, “Improving the Testing Efficiency of Selenium-based Load Tests,” in Proceedings of the 14th IEEE/ACM International Workshop on Automation of Software Test (AST), 2019.

2018

  1. H. Li, T.-H. P. Chen, A. E. Hassan, M. Nasser, and P. Flora, “Adopting Autonomic Computing Capabilities in Existing Large-Scale Systems: An Industrial Experience Report,” in Proceedings of the 40th International Conference on Software Engineering (ICSE-SEIP), 2018.
  2. H. Li and Z. Zhang, “Predicting the Receivers of Football Passes,” in Machine Learning and Data Mining for Sports Analytics (MLSA), 2018.
  3. H. Li, T.-H. P. Chen, W. Shang, and A. E. Hassan, “Studying software logging using topic models,” Empirical Software Engineering (EMSE), vol. 23, no. 5, pp. 2655–2694, 2018.

2017

  1. H. Li, W. Shang, Y. Zou, and A. E. Hassan, “Towards just-in-time suggestions for log changes,” Empirical Software Engineering (EMSE), vol. 22, no. 4, pp. 1831–1865, 2017.
  2. H. Li, W. Shang, and A. E. Hassan, “Which log level should developers choose for a new logging statement?,” Empirical Software Engineering (EMSE), vol. 22, no. 4, pp. 1684–1716, 2017.