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Ding, Z., Tang, Y., Cheng, X., Li, H., & Shang, W. (2024). LoGenText-Plus : Improving Neural Machine Translation Based Logging Texts Generation with Syntactic Templates. ACM Transactions on Software Engineering and Methodology, 33(2), 38 (45 pages). Lien externe
Liao, L., Li, H., Shang, W., Sporea, C., Toma, A., & Sajedi, S. (décembre 2023). Adapting Performance Analytic Techniques in a Real-World Database-Centric System: An Industrial Experience Report [Communication écrite]. 31st ACM Joint Meeting of the European Software Engineering Conference / Symposium on the Foundations-of-Software-Engineering (ESEC/FSE), San Francisco, CA San Francisco, CA. Lien externe
Chen, J., Ding, Z., Tang, Y., Sayagh, M., Li, H., Adams, B., & Shang, W. (décembre 2023). IoPV : on inconsistent option performance variations [Communication écrite]. 2023 ESEC/FSE Conferences, San Francisco, CA, USA (13 pages). Lien externe
Dai, H., Tang, Y., Li, H., & Shang, W. PILAR: Studying and Mitigating the Influence of Configurations on Log Parsing [Communication écrite]. 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE 2023), Melbourne, Australia. Lien externe
Ding, Z., Li, H., Shang, W., & Chen, T.-H. P. (2023). Towards Learning Generalizable Code Embeddings Using Task-agnostic Graph Convolutional Networks. ACM Transactions on Software Engineering and Methodology, 32(2), 1-43. Lien externe
Lamothe, M., Shang, W., & Chen, T.-H. P. (2022). A3: Assisting Android API Migrations Using Code Examples. IEEE Transactions on Software Engineering, 48(2), 417-431. Lien externe
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. Lien externe
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. Lien externe
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), 1-40. Lien externe
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. Lien externe
Ding, Z., Li, H., & Shang, W. (mars 2022). LoGenText: Automatically generating logging texts using neural machine translation [Communication écrite]. IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER 2022), Honolulu, HI, USA. Lien externe
Zhang, H., Tang, Y., Lamothe, M., Li, H., & Shang, W. (2022). Studying logging practice in test code. Empirical Software Engineering, 27(4), 83 (45 pages). Lien externe
Li, Z., Li, H., Chen, T.-H. P., & Shang, W. (mai 2021). DeepLV: Suggesting log levels using ordinal based neural networks [Communication écrite]. 43rd International Conference on Software Engineering (ICSE 2021) (12 pages). Lien externe
Quach, S., Lamothe, M., Kamei, Y., & Shang, W. (2021). An empirical study on the use of SZZ for identifying inducing changes of non-functional bugs. Empirical Software Engineering, 26(4). Lien externe
Quach, S., Lamothe, M., Adams, B., Kamei, Y., & Shang, W. (2021). Evaluating the impact of falsely detected performance bug-inducing changes in JIT models. Empirical Software Engineering, 26(5). Lien externe
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. Lien externe
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. Lien externe
Lamothe, M., Gueheneuc, Y. G., & Shang, W. (2021). A Systematic Review of API Evolution Literature. ACM Computing Surveys, 54(8), 1-36. Lien externe
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. Lien externe
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. Lien externe
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(5), 4130-4160. Lien externe
Lamothe, M., & Shang, W. (juin 2020). When APIs are intentionally bypassed [Communication écrite]. 42nd ACM/IEEE International Conference on Software Engineering, Seoul, South Korea. Lien externe
Lamothe, M., & Shang, W. (mai 2018). Exploring the use of automated API migrating techniques in practice [Communication écrite]. 15th International Conference on Mining Software Repositories, Gothenburg, Sweden. Lien externe
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. Lien externe
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. Lien externe
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. Lien externe
Shang, W., Jiang, Z. M., Adams, B., Hassan, A. E., Godfrey, M. W., Nasser, M., & Flora, P. (2014). An exploratory study of the evolution of communicated information about the execution of large software systems. Journal of Software: Evolution and Process, 26(1), 3-26. Lien externe
Shang, W., Jiang, Z. M., Hemmati, H., Adams, B., Hassan, A. E., & Martin, P. (mai 2013). Assisting big data analytics developers when cloud deploying hadoop applications [Communication écrite]. 35th International Conference on Software Engineering (ICSE 2013), San Francisco, CA, USA. Lien externe
