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Collaboration networkThis graph maps the connections between all the collaborators of {}'s publications listed on this page.
Each link represents a collaboration on the same publication. The thickness of the link represents the number of collaborations.
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Hold down the "Ctrl" key or the "⌘" key while clicking on the nodes to open the list of this person's publications.
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.
In the context of this page, the word cloud was generated from the publications of the author {}. The words in this cloud come from the titles, abstracts, and keywords of the author's articles and research papers. By analyzing this word cloud, you can get an overview of the most recurring and significant topics and research areas in the author's work.
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.
Openja, M., Khomh, F., Foundjem, A. T., Jiang, Z. M., Abidi, M., & Hassan, A. E. (2024). An empirical study of testing machine learning in the wild. ACM Transactions on Software Engineering and Methodology. External link
Lyu, Y., Li, H., Jiang, Z. M., & Hassan, A. E. (2024). On the Model Update Strategies for Supervised Learning in AIOps Solutions. ACM Transactions on Software Engineering and Methodology, -. External link
Morovati, M. M., Nikanjam, A., Tambon, F., Khomh, F., & Jiang, Z. M. (2024). Bug characterization in machine learning-based systems. Empirical Software Engineering, 29(1), 14 (29 pages). External link
Bhatia, A., Eghan, E. E., Grichi, M., Cavanagh, W. G., Jiang, Z. M., & Adams, B. (2023). Towards a change taxonomy for machine learning pipelines Empirical study of ML pipelines and forks related to academic publications. Empirical Software Engineering, 28(3), 60 (34 pages). External link
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), 1-38. 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
Shihab, E., Hassan, A. E., Adams, B., & Jiang, Z. M. (2012, November). An industrial study on the risk of software changes [Paper]. 20th ACM SIGSOFT International Symposium on the Foundations of Software Engineering (FSE 2012), Cary, NC, United states. External link
