Long-Term Productivity Based on Science, not Preference

TL;DR

This paper advocates for a scientific, evidence-based approach to decision-making in scientific software development, emphasizing long-term productivity over personal preference.

Contribution

It introduces a framework for assessing productivity in scientific software development using empirical evidence and proposes measuring long-term output benefits.

Findings

Emphasizes the importance of long-term productivity in software decisions.

Proposes a preliminary definition and potential measurement of productivity.

Suggests generating artifacts from codified knowledge to improve productivity.

Abstract

This position paper argues that decisions on processes, tools, techniques and software artifacts (such as user manuals, unit tests, design documents and code) for scientific software development should be driven by science, not by personal preference. Decisions should not be based on anecdotal evidence, gut instinct or the path of least resistance. Moreover, decisions should vary depending on the users and the context. In most cases of interest, this means that a longer term view should be adopted. We need to use a scientific approach based on unambiguous definitions, empirical evidence, hypothesis testing and rigorous processes. By developing an understanding of where input hours are spent, what most contributes to user satisfaction, and how to leverage knowledge produced, we can determine what interventions have the greatest value relative to the invested effort. We will be able to recommend software production processes that justify their value because the long-term output benefits are high compared to the required input resources. A preliminary definition of productivity is presented, along with ideas on how to potentially measure this quality. We briefly explore the idea of improving productivity via an approach where all artifacts are generated from codified knowledge.