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Software Evolution for Machine Learning

Refactorings and Technical Debt in Machine Learning Systems


Machine Learning (ML), including Deep Learning (DL), systems, i.e., those with ML capabilities, are pervasive in today’s data-driven society. Such systems are complex; they are comprised of ML models and many subsystems that support learning processes. As with other complex systems, ML systems are prone to classic technical debt issues, especially when such systems are long-lived, but they also exhibit debt specific to these systems. Unfortunately, there is a gap in knowledge about how ML systems actually evolve and are maintained. In this project, we fill this gap by studying refactorings, i.e., source-to-source semantics-preserving program transformations, performed in real-world, open-source software, and the technical debt issues they alleviate. We analyzed 26 projects, consisting of 4.2 MLOC, along with 327 manually examined code patches. The results indicate that developers refactor these systems for a variety of reasons, both specific and tangential to ML, some refactorings correspond to established technical debt categories, while others do not, and code duplication is a major cross-cutting theme that particularly involved ML configuration and model code, which was also the most refactored. We also introduce 14 and 7 new ML-specific refactorings and technical debt categories, respectively, and propose several recommendations, best practices, and anti-patterns. The results can potentially assist practitioners, tool developers, and educators in facilitating long-term ML system usefulness.


Below is a list of publications. My and my research students‘ names are boldfaced, undergraduate students are italicized, and female students are underlined:

Yiming TangRaffi Khatchadourian, Mehdi Bagherzadeh, Rhia SinghAjani Stewart, and Anita Raja. An empirical study of refactorings and technical debt in Machine Learning systems. In International Conference on Software Engineering, ICSE ’21, pages 238–250. IEEE/ACM, IEEE, May 2021. (138/615; 22% acceptance rate). [ bib | DOI | data | slides | http ]

Study Data


Our dataset is hosted on Zenodo.


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