Software Engineering Practices in Academia: Promoting the 3Rs—Readability, Resilience, and Reuse

Abstract

Over the past decade as data science has become integral to the research workflow, we, like many others, have learned that good data science requires high-quality software engineering. Unfortunately, our experience is that many data science projects can be limited by the absence of software engineering processes. We advocate that data science projects should incorporate what we call the 3Rs of software engineering: readability (human understandable codes), resilience (fails rarely/gracefully), and reuse (can easily be used by others and can be embedded in other software). This article discusses engineering practices that promote 3R software in academia. We emphasize that best practices in academia may differ from those in industry because of substantial differences in project scope (most academic projects have a single developer who is the sole user) and the reward systems in place in academia. We provide a framework for selecting a level of software engineering rigor that aligns well with the project scope, something that may change over time. We further discuss how to improve training in software engineering skills in an academic environment and how to build communities of practice that span across disciplines.

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