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Ensuring the long-term reproducibility of data analyses requires resultsstability tests to verify that analysis results remain within acceptablevariation bounds despite inevitable software updates and hardware evolutions.This paper introduces a numerical variability approach for results stabilitytests, which determines acceptable variation bounds using random rounding offloating-point calculations. By applying the resulting stability test to\fmriprep, a widely-used neuroimaging tool, we show that the test is sensitiveenough to detect subtle updates in image processing methods while remainingspecific enough to accept numerical variations within a reference version ofthe application. This result contributes to enhancing the reliability andreproducibility of data analyses by providing a robust and flexible method forstability testing.

A numerical variability approach to results stability tests and its application to neuroimaging