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Evaluating the computational reproducibility of data analysis pipelines hasbecome a critical issue. It is, however, a cumbersome process for analyses thatinvolve data from large populations of subjects, due to their computational andstorage requirements. We present a method to predict the computationalreproducibility of data analysis pipelines in large population studies. Weformulate the problem as a collaborative filtering process, with constraints onthe construction of the training set. We propose 6 different strategies tobuild the training set, which we evaluate on 2 datasets, a synthetic onemodeling a population with a growing number of subject types, and a real oneobtained with neuroinformatics pipelines. Results show that one samplingmethod, "Random File Numbers (Uniform)" is able to predict computationalreproducibility with a good accuracy. We also analyze the relevance ofincluding file and subject biases in the collaborative filtering model. Weconclude that the proposed method is able to speedup reproducibilityevaluations substantially, with a reduced accuracy loss.

Predicting computational reproducibility of data analysis pipelines in large population studies using collaborative filtering