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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), 38 pages. External link
Morovati, M. M., Nikanjam, A., Khomh, F., & Jiang, Z. M. (2023). Bugs in machine learning-based systems: a faultload benchmark. Empirical Software Engineering, 28(3), 33 pages. 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, 3-26. 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