On the Co-evolution of ML Pipelines and Source Code - Empirical Study of DVC Projects

The growing popularity of machine learning (ML) applications has led to the introduction of software engineering tools such as Data Versioning Control (DVC), MLFlow and Pachyderm that enable versioning ML data, models, pipelines and model evaluation metrics. Since these versioned ML artifacts need to be synchronized not only with each other, but also with the source and test code of the software applications into which the models are integrated, prior findings on co-evolution and coupling between software artifacts might need to be revisited. Hence, in order to understand the degree of coupling between ML-related and other software artifacts, as well as the adoption of ML versioning features, this paper empirically studies the usage of DVC in 391 Github projects, 25 of which in detail. Our results show that more than half of the DVC files in a project are changed at least once every one-tenth of the project's lifetime. Furthermore, we observe a tight coupling between DVC files and other artifacts, with 1/4 pull requests changing source code and 1/2 pull requests changing tests requiring a change to DVC files. As additional evidence of the observed complexity associated with adopting ML-related software engineering tools like DVC, an average of 78% of the studied projects showed a non-constant trend in pipeline complexity.