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Word cloudA word cloud is a visual representation of the most frequently used words in a text or a set of texts. The words appear in different sizes, with the size of each word being proportional to its frequency of occurrence in the text. The more frequently a word is used, the larger it appears in the word cloud. This technique allows for a quick visualization of the most important themes and concepts in a text.
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The word cloud is a useful tool for identifying trends and main themes in a corpus of texts, thus facilitating the understanding and analysis of content in a visual and intuitive way.
Chen, J., Ding, Z., Tang, Y., Sayagh, M., Li, H., Adams, B., & Shang, W. (2023, December). IoPV : on inconsistent option performance variations [Paper]. 2023 ESEC/FSE Conferences, San Francisco, CA, USA (13 pages). External link
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). External link
Dai, H., Tang, Y., Li, H., & Shang, W. PILAR: Studying and Mitigating the Influence of Configurations on Log Parsing [Paper]. 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE 2023), Melbourne, Australia. External link
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. External link
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. External link
Ding, Z., Li, H., & Shang, W. (2022, March). LoGenText: Automatically generating logging texts using neural machine translation [Paper]. IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER 2022), Honolulu, HI, USA. External link
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. External link
Liao, L., Eismann, S., Li, H., Bezemer, C.-P., Costa, D. E., van Hoorn, A., & Shang, W. (2025, April). Early Detection of Performance Regressions by Bridging Local Performance Data and Architectural Models [Paper]. 47th International Conference on Software Engineering (ICSE 2025), Ottawa, ON, Canada. External link
Liao, L., Li, H., Shang, W., Sporea, C., Toma, A., & Sajedi, S. (2023, December). Adapting Performance Analytic Techniques in a Real-World Database-Centric System: An Industrial Experience Report [Paper]. 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. External link
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. External link
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. External link
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. External link
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. External link
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, 48(12), 4986-5006. External link
Li, Z., Li, H., Chen, T.-H. P., & Shang, W. (2021, May). DeepLV: Suggesting log levels using ordinal based neural networks [Paper]. 43rd International Conference on Software Engineering (ICSE 2021) (12 pages). External link
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. External link
Lamothe, M., Gueheneuc, Y. G., & Shang, W. (2021). A Systematic Review of API Evolution Literature. ACM Computing Surveys, 54(8), 1-36. External link
Liao, L., Chen, J., Li, H., Zeng, Y., Shang, W., Sporea, C., Toma, A., & Sajedi, S. (2021). TSE2021 replication package [Dataset]. External link
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. External link
Lamothe, M., & Shang, W. (2020, June). When APIs are intentionally bypassed [Paper]. 42nd ACM/IEEE International Conference on Software Engineering, Seoul, South Korea. External link
Lamothe, M., & Shang, W. (2018, May). Exploring the use of automated API migrating techniques in practice [Paper]. 15th International Conference on Mining Software Repositories, Gothenburg, Sweden. External link
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. External link
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. External link
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. External link
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). External link
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). External link
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. External link
Shang, W., Jiang, Z. M., Hemmati, H., Adams, B., Hassan, A. E., & Martin, P. (2013, May). Assisting big data analytics developers when cloud deploying hadoop applications [Paper]. 35th International Conference on Software Engineering (ICSE 2013), San Francisco, CA, USA. External link
Xia, Y., Liao, L., Chen, J., Li, H., & Shang, W. (2024). Reducing the Length of Field-replay Based Load Testing. IEEE Transactions on Software Engineering, 3408079 (17 pages). External link
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. External link
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). External link
