Obstacles and opportunities with using visual and domain-specific languages in scientific programming

Abstract

Scientific discovery is the lifeblood of technological progress, and end-user programming in turn is increasingly essential to modern science. In order to uncover opportunities to facilitate scientific programming, we interviewed scientists about their choice of tools and languages, as well as the obstacles resulting from those choices. We focused on domain-specific languages (DSLs), particularly visual DSLs, because prior empirical studies had not explored scientists' DSL use in detail. We found that DSLs were indeed used by most of these scientists, and in fact it was typical for scientific projects to use an increasing number of DSLs over time. Our study extended some findings from related work, and it identified obstacles not previously uncovered. In particular, we found that scientists often struggled with managing data complexity, as well as with using version control systems. Our study revealed several opportunities to improve DSLs and related tools, such as for helping scientists to cope with data complexity and for helping them to foresee problems when choosing a language.