The Changing Nature of Computational Science Software

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 (multicore 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.

How Different is Test Case Prioritization for Open and Closed Source Projects?

“…Many projects in GitHub are very small or are out of maintenance, which may not have enough information for our experiments. To avoid these traps, we implement the GitHub "sanity check", which is introduced in the literature [34]- [36].…”

“…• We also explored software from the computational science community. Computational Science (hereafter, CS) field studies and develops software to explore astronomy, astrophysics, chemistry, economics, genomics, molecular biology, oceanography, physics, political science, and many engineering fields [34]. After the above analysis, we find ten projects from computational science and twenty projects from software engineering that suitable for our analysis: see Table 3.…”