The Changing Nature of Computational Science Software

TL;DR

This paper analyzes 59 GitHub projects to evaluate 13 beliefs about computational science software, revealing that many traditional assumptions are outdated and highlighting the evolving nature of CS software development.

Contribution

It provides empirical evidence challenging existing beliefs about CS software, informing future tool development and research directions.

Findings

Majority of traditional beliefs are unsupported by data

CS software development is undergoing significant change

Implications for tool support and research directions

Abstract

How should software engineering be adapted for Computational Science (CS)? If we understood that, then we could better support software sustainability, verifiability, reproducibility, comprehension, and usability for CS community. For example, improving the maintainability of the CS code could lead to: (a) faster adaptation of scientific project simulations to new and efficient hardware (multi-core and heterogeneous systems); (b) better support for larger teams to co-ordinate (through integration with interdisciplinary teams); and (c) an extended capability to model complex phenomena. In order to better understand computational science, this paper uses quantitative evidence (from 59 CS projects in Github) to check 13 published beliefs about CS. These beliefs reflect on (a) the nature of scientific challenges; (b) the implications of limitations of computer hardware; and (c) the cultural environment of scientific software development. What we found was, using this new data from Github, only a minority of those older beliefs can be endorsed. More than half of the pre-existing beliefs are dubious, which leads us to conclude that the nature of CS software development is changing. Further, going forward, this has implications for (1) what kinds of tools we would propose to better support computational science and (2) research directions for both communities.