Reproducible Science and Deep Software Variability

Mathieu Acher (1, 2)

1 IUF - Institut universitaire de France
2 DiverSe - Diversity-centric Software Engineering

Biology, medicine, physics, astrophysics, chemistry: all these scientific domains need to process large amount of data with more and more complex software systems. For achieving reproducible science, there are several challenges ahead involving multidisciplinary collaboration and socio-technical innovation with software at the center of the problem. Despite the availability of data and code, several studies report that the same data analyzed with different software can lead to different results. I am seeing this problem as a manifestation of deep software variability: many factors (operating system, third-party libraries, versions, workloads, compile-time options and flags, etc.) themselves subject to variability can alter the results, up to the point it can dramatically change the conclusions of some scientific studies. In this keynote, I argue that deep software variability is a threat and also an opportunity for reproducible science. I first outline some works about (deep) software variability, reporting on preliminary evidence of complex interactions between variability layers. I then link the ongoing works on variability modelling and deep software variability in the quest for reproducible science.

Domaines: Génie logiciel [cs.SE]

Mathieu Acher. Reproducible Science and Deep Software Variability. VaMoS 2022 - 16th International Working Conference on Variability Modelling of Software-Intensive Systems, Feb 2022, Florence, Italy. pp.1-2. ⟨hal-03528889⟩