Over the years, researchers capitalized on the repetitiveness of software changes to automate many software evolution tasks. Despite the extraordinary rise in popularity of Python-based ML systems, they do not benefit from these advances. Without knowing what are the repetitive changes that ML developers make, researchers, tool, and library designers miss opportunities for automation, and ML developers fail to learn and use best coding practices. To fill the knowledge gap and advance the science and tooling in ML software evolution, we conducted the first and most fine-grained study on code change patterns in a diverse corpus of 1000 top-rated ML systems comprising 58 million SLOC. To conduct this study we reuse, adapt, and improve upon the state-of-the-art repetitive change mining techniques. Our novel tool, R-CPATMINER, mines over 4M commits and constructs 350K fine-grained change graphs and detects 28K change patterns. Using thematic analysis, we identified 22 pattern groups and we reveal 4 major trends of how ML developers change their code. We surveyed 650 ML developers to further shed light on these patterns and their applications, and we received a 15% response rate. We present actionable, empirically-justified implications for four audiences: (i) researchers, (ii) tool builders, (iii) ML library vendors, and (iv) developers and educators.
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