Heng Li

Heng Li

PhD, Eng., Assistant Professor

Publications

2025

  • S.-W. Huang, X. Wu, and H. Li, “LogLSHD: Fast Log Parsing with Locality-Sensitive Hashing and Dynamic Time Warping,” in Companion Proceedings of the 33rd ACM International Conference on the Foundations of Software Engineering, 2025.
  • P. L. Foalem, L. D. Silva, F. Khomh, H. Li, and E. Merlo, “Logging Requirement for Continuous Auditing of Responsible Machine Learning-based Applications,” Empirical Software Engineering (EMSE), 2025.
  • B. Jin, H. Li, and Y. Zou, “Impact of Extensions on Browser Performance: An Empirical Study on Google Chrome,” Empirical Software Engineering (EMSE), pp. 36 pages, 2025 [Online]. Available at: https://arxiv.org/abs/2404.06827
  • R. Aghili, H. Li, and F. Khomh, “Protecting Privacy in Software Logs: What Should be Anonymized?,” in Proceedings of the ACM on Software Engineering (FSE)(accepted), 2025 [Online]. Available at: https://arxiv.org/html/2409.11313v1
  • S. Noei, H. Li, and Y. Zou, “An Empirical Study on Release-Wise Refactoring Patterns,” in Proceedings of the ACM on Software Engineering (FSE)(accepted), 2025.
  • Q. Qin, R. Aghili, H. Li, and E. Merlo, “Preprocessing is All You Need: Boosting the Performance of Log Parsers With a General Preprocessing Framework,” in Proceedings of the 32nd IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)(accepted), 2025, pp. 310–320 [Online]. Available at: https://arxiv.org/html/2412.05254v1
  • G. Caumartin, Q. Qin, H. Li, and D. Costa, “Exploring the Potential of Llama Models in Automated Code Refinement: A Replication Study,” in Proceedings of the 32nd IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)(accepted), 2025, pp. 681–692 [Online]. Available at: https://www.arxiv.org/abs/2412.02789
  • Q. Qin, H. Li, E. Merlo, and M. Lamothe, “Automated, Unsupervised, and Auto-parameterized Inference of Data Patterns and Anomaly Detection,” in Proceedings of the 47th International Conference on Software Engineering (ICSE) (accepted), 2025 [Online]. Available at: https://arxiv.org/html/2412.05240v1
  • L. Liao, S. Eismann, H. Li, C.-P. Bezemer, D. E. Costa, A. van Hoorn, and W. Shang, “Early Detection of Performance Regressions by Bridging Local Performance Data and Architectural Models,” in Proceedings of the 47th International Conference on Software Engineering (ICSE) (accepted), 2025 [Online]. Available at: https://arxiv.org/abs/2408.08148

2024

  • S. Noei, H. Li, and Y. Zou, “Detecting Refactoring Commits in Machine Learning Python Projects: A Machine Learning-Based Approach,” ACM Transactions on Software Engineering and Methodology (TOSEM) (accepted), vol. 34, no. 3, pp. 1–25, 2024 [Online]. Available at: https://arxiv.org/pdf/2404.06572
  • R. Aghili, Q. Qin, H. Li, and F. Khomh, “Understanding Web Application Workloads and Their Applications: Systematic Literature Review and Characterization,” in Proceedings of the 40th IEEE International Conference on Software Maintenance and Evolution (ICSME) (accepted), 2024, pp. 474–486 [Online]. Available at: https://arxiv.org/abs/2409.12299
  • Y. Xia, L. Liao, J. Chen, H. Li, and W. Shang, “Reducing the Length of Field-replay Based Load Testing,” IEEE Transactions on Software Engineering (TSE), vol. 50, no. 8, pp. 1967–1983, 2024.
  • A. Ghadesi, M. Lamothe, and H. Li, “What Causes Exceptions in Machine Learning Applications? Mining Machine Learning-Related Stack Traces on Stack Overflow,” Empirical Software Engineering (EMSE), vol. 29, no. 107, pp. 37 pages, 2024 [Online]. Available at: https://arxiv.org/pdf/2304.12857
  • X. Wu, E. Laufer, H. Li, F. Khomh, S. Srinivasan, and J. Luo, “Characterizing and Classifying Developer Forum Posts with their Intentions,” Empirical Software Engineering (EMSE), vol. 29, no. 84, pp. 34 pages, 2024 [Online]. Available at: https://arxiv.org/abs/2312.14279
  • Y. Lyu, H. Li, Z. M. Jiang, and A. E. Hassan, “On the Model Update Strategies for Supervised Learning in AIOps Solutions,” ACM Transactions on Software Engineering and Methodology (TOSEM), vol. 33, no. 7, pp. 1–38, 2024 [Online]. Available at: https://arxiv.org/abs/2311.03213
  • X. Wu, H. Li, N. Yoshioka, H. Washizaki, and F. Khomh, “Refining GPT-3 Embeddings with a Siamese Structure for Technical Post Duplicate Detection,” in Proceedings of the 31st IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), 2024, pp. 1–12.
  • M. A. Batoun, M. Sayagh, R. Aghili, A. Ouni, and H. Li, “A Literature Review and Existing Challenges on Software Logging Practices - From the Creation to the Analysis of Software Logs,” Empirical Software Engineering (EMSE), vol. 29, no. 103, pp. 61 pages, 2024.
  • P. L. Foalem, F. Khomh, and H. Li, “Studying Logging Practice in Machine Learning-based Applications,” Information and Software Technology (IST), vol. 170, pp. 1–17, 2024 [Online]. Available at: https://arxiv.org/pdf/2301.04234.pdf

2023

  • S. Noei, H. Li, S. Georgiou, and Y. Zou, “An Empirical Study of Refactoring Rhythms and Tactics in the Software Development Process,” IEEE Transactions on Software Engineering (TSE), vol. 49, no. 12, pp. 5103–5119, 2023.
  • Z. Ding, Y. Tang, X. Cheng, H. Li, and W. Shang, “LoGenText-Plus: Improving Neural Machine Translation-based Logging Texts Generation with Syntactic Templates,” ACM Transactions on Software Engineering and Methodology (TOSEM), vol. 2, no. 33, pp. 1–45, 2023.
  • B. Chembakottu, H. Li, and F. Khomh, “A Large-Scale Exploratory Study of Android Sports Apps in the Google Play Store,” Information and Software Technology (IST), vol. 164, no. 107321, pp. 1–18, 2023.
  • R. Aghili, H. Li, and F. Khomh, “Studying the characteristics of AIOps projects on GitHub,” Empirical Software Engineering (EMSE), vol. 28, no. 143, pp. 49 pages, 2023.
  • L. Liao, H. Li, W. Shang, C. Sporea, A. Toma, and S. Sajedi, “Adapting Performance Analytic Techniques in a Real-World Database-Centric System: An Industrial Experience Report,” in ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE), Industrial Track, 2023, pp. 1855–1866.
  • X. Wu, H. Li, and F. Khomh, “On the Effectiveness of Log Representation for Log-based Anomaly Detection,” Empirical Software Engineering (EMSE), vol. 28, no. 137, pp. 39 pages, 2023.
  • A. H. Yahmed, A. A. Abbassi, A. Nikanjam, H. Li, and F. Khomh, “Deploying Deep Reinforcement Learning Systems: A Taxonomy of Challenges,” in Proceedings of the 39th IEEE International Conference on Software Maintenance and Evolution (ICSME), 2023, pp. 26–38.
  • J. Chen, Z. Ding, Y. Tang, M. Sayagh, H. Li, B. Adams, and W. Shang, “IoPV: On Inconsistent Option Performance Variations,” in ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE), 2023, pp. 845–857.
  • F. Yousefifeshki, H. Li, and F. Khomh, “Studying the challenges of developing hardware description language programs,” Information and Software Technology (IST), vol. 159, 2023 [Online]. Available at: https://www.sciencedirect.com/science/article/pii/S0950584923000502
  • Z. Ding, Y. Tang, Y. Li, H. Li, and W. Shang, “On the Temporal Relations between Logging and Code,” in Proceedings of the 45rd International Conference on Software Engineering (ICSE), 2023, pp. 843–854.
  • 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, pp. 818–829.
  • 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), vol. 32, no. 2, pp. 1–43, 2023.

2022

  • 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, pp. 59–70.
  • M. Raed, H. Li, F. Khomh, and L. Tidjon, “Bug Characteristics in Quantum Software Ecosystem,” arXiv preprint arXiv:2204.11965, 2022.
  • 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, pp. 190–200.
  • H. Zhang, Y. Tang, M. Lamothe, H. Li, and W. Shang, “Studying Logging Practice in Test Code,” Empirical Software Engineering (EMSE), vol. 27, no. 83, pp. 45 pages, 2022.
  • 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. 63, pp. 38 pages, 2022.
  • 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, pp. 349–360.
  • 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, pp. 890–901.
  • 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.
  • 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), vol. 48, no. 12, pp. 4986–5006, 2022.
  • 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 (TSE), vol. 48, no. 12, pp. 4907–4924, 2022.
  • M. Lamothe, H. Li, and W. Shang, “Assisting Example-based API Misuse Detection via Complementary Artificial Examples,” IEEE Transactions on Software Engineering (TSE), vol. 48, no. 9, pp. 3410–3422, 2022.
  • 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), vol. 48, no. 9, pp. 3227–3241, 2022.
  • 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), vol. 48, no. 7, pp. 2359–2375, 2022.

2021

  • 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.
  • 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.
  • 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.
  • 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

  • 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.
  • 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.
  • 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.
  • 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.
  • 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

  • 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, pp. 14–20.

2018

  • 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.
  • H. Li and Z. Zhang, “Predicting the Receivers of Football Passes,” in Machine Learning and Data Mining for Sports Analytics (MLSA), 2018.
  • 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

  • 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.
  • 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.