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