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. Retrieved from https://doi.org/10.1145/3506695
Directory of Experts
Li, Heng
Directory of Experts
Li, Heng
Directory of Experts
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Journal article (18)
Conference paper (7)
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Heng Li (26)
- Journal articles (18)
- 2022
Journal article Journal article 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. Retrieved from https://doi.org/10.1109/TSE.2021.3093246Journal article 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. Retrieved from https://doi.org/10.1007/s10664-022-10118-5Journal article 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. Retrieved from https://doi.org/10.1109/TSE.2021.3083715Journal article Zhang, H., Tang, Y., Lamothe, M., Li, H. & Shang, W. (2022). Studying logging practice in test code. Empirical Software Engineering, 27(4), 45 pages. Retrieved from https://doi.org/10.1007/s10664-022-10139-0
- 2021
Journal article 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. Retrieved from https://doi.org/10.1145/3447876Journal article Gujral, H., Lal, S. & Li, H. (2021). An exploratory semantic analysis of logging questions. Journal of Software: Evolution and Process, 33(7), 35 pages. Retrieved from https://doi.org/10.1002/smr.2361Journal article 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. Retrieved from https://doi.org/10.1109/TSE.2020.2970422Journal article 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. Retrieved from https://doi.org/10.1109/TSE.2021.3058985Journal article 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. Retrieved from https://doi.org/10.1109/TSE.2021.3131529Journal article 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. Retrieved from https://doi.org/10.1109/TSE.2021.3129688
- 2020
Journal article 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. Retrieved from https://doi.org/10.1007/s10664-020-09822-xJournal article 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. Retrieved from https://doi.org/10.1109/TSE.2020.3007554Journal article 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. Retrieved from https://doi.org/10.1145/3385187Journal article 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. Retrieved from https://doi.org/10.1007/s10664-020-09866-z
- 2018
Journal article 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. Retrieved from https://doi.org/10.1007/s10664-018-9595-8
- 2017
Journal article 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. Retrieved from https://doi.org/10.1007/s10664-016-9467-zJournal article 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. Retrieved from https://doi.org/10.1007/s10664-016-9456-2
- 2022
- Conference papers (7)
- 2022
Conference paper Ding, Z., Li, H. & Shang, W. (2022). LoGenText: Automatically generating logging texts using neural machine translation. Paper presented at the IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER 2022), Honolulu, HI, USA (pp. 349-360). Retrieved from https://doi.org/10.1109/SANER53432.2022.00051Conference paper Hassan, S., Li, H. & Hassan, A.E. (2022). On the importance of performing app analysis within peer groups. Paper presented at the IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER 2022), Honolulu, HI, USA (pp. 890-901). Retrieved from https://doi.org/10.1109/SANER53432.2022.00107
- 2021
Conference paper Li, Z., Li, H., Chen, T.-H.P. & Shang, W. (2021). DeepLV: Suggesting log levels using ordinal based neural networks. Paper presented at the 43rd International Conference on Software Engineering (ICSE 2021) (12 pages). Retrieved from https://conf.researchr.org/details/icse-2021/icse-2021-papers/98/DeepLV-Suggesting-Log-Levels-Using-Ordinal-Based-Neural-NetworksConference paper 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. Paper presented at the IEEE International Conference on Software Maintenance and Evolution (ICSME 2021), Luxembourg, Netherlands (pp. 343-354). Retrieved from https://doi.org/10.1109/ICSME52107.2021.00037
- 2019
Conference paper 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. Paper presented at the 14th IEEE/ACM International Workshop on Automation of Software Test (AST 2019), Montréal, Québec. Retrieved from https://doi.org/10.1109/AST.2019.00008
- 2018
Conference paper 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. Paper presented at the 40th International Conference on Software Engineering (ICSE-SEIP 2018), Gothenburg, Sweden (10 pages). Retrieved from https://doi.org/10.1145/3183519.3183544Conference paper Li, H. & Zhang, Z. (2018). Predicting the receivers of football passes. Paper presented at the Machine Learning and Data Mining for Sports Analytics (MLSA 2018), Dublin, Ireland (pp. 167-177). Retrieved from https://doi.org/10.1007/978-3-030-17274-9_15
- 2022
- Theses (1)
- 2018
Thesis Li, H. (2018). Mining development knowledge to understand and support software logging practices (Ph.D. Thesis). Retrieved from http://hdl.handle.net/1974/25854
- 2018
