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The Art and Practice of Data Science Pipelines: A Comprehensive Study of Data Science Pipelines In Theory, In-The-Small, and In-The-Large., , и . ICSE, стр. 2091-2103. ACM, (2022)Do the machine learning models on a crowd sourced platform exhibit bias? an empirical study on model fairness., и . ESEC/SIGSOFT FSE, стр. 642-653. ACM, (2020)Fix Fairness, Don't Ruin Accuracy: Performance Aware Fairness Repair using AutoML., , и . ESEC/SIGSOFT FSE, стр. 502-514. ACM, (2023)23 shades of self-admitted technical debt: an empirical study on machine learning software., , , , , и . ESEC/SIGSOFT FSE, стр. 734-746. ACM, (2022)Boa meets python: a boa dataset of data science software in python language., , , и . MSR, стр. 577-581. IEEE / ACM, (2019)Are Prompt Engineering and TODO Comments Friends or Foes? An Evaluation on GitHub Copilot., , , , , и . ICSE, стр. 219:1-219:13. ACM, (2024)Fairify: Fairness Verification of Neural Networks., и . ICSE, стр. 1546-1558. IEEE, (2023)Fair preprocessing: towards understanding compositional fairness of data transformers in machine learning pipeline., и . ESEC/SIGSOFT FSE, стр. 981-993. ACM, (2021)Towards Safe ML-Based Systems in Presence of Feedback Loops., , и . SE4SafeML@SIGSOFT FSE, стр. 18-21. ACM, (2023)Towards Understanding Fairness and its Composition in Ensemble Machine Learning., , и . ICSE, стр. 1533-1545. IEEE, (2023)