Evaluation of software impact designed for biomedical research: Are we measuring what's meaningful?

TL;DR

This paper examines current practices and challenges in measuring the impact of biomedical research software, highlighting the importance of nuanced evaluation strategies and infrastructure to improve software usage and community engagement.

Contribution

It provides a survey of evaluation practices within the biomedical software community and proposes guidelines to better capture meaningful impact beyond traditional metrics.

Findings

Infrastructure like social media and documentation correlates with higher usage.

Developers value impact analysis but lack resources to perform it.

Nuanced evaluation strategies are needed for small or specialized tools.

Abstract

Software is vital for the advancement of biology and medicine. Analysis of usage and impact metrics can help developers determine user and community engagement, justify additional funding, encourage additional use, identify unanticipated use cases, and help define improvement areas. However, there are challenges associated with these analyses including distorted or misleading metrics, as well as ethical and security concerns. More attention to the nuances involved in capturing impact across the spectrum of biological software is needed. Furthermore, some tools may be especially beneficial to a small audience, yet may not have compelling typical usage metrics. We propose more general guidelines, as well as strategies for more specific types of software. We highlight outstanding issues regarding how communities measure or evaluate software impact. To get a deeper understanding of current practices for software evaluations, we performed a survey of participants in the Informatics Technology for Cancer Research (ITCR) program funded by the National Cancer Institute (NCI). We also investigated software among this community and others to assess how often infrastructure that supports such evaluations is implemented and how this impacts rates of papers describing usage of the software. We find that developers recognize the utility of analyzing software usage, but struggle to find the time or funding for such analyses. We also find that infrastructure such as social media presence, more in-depth documentation, the presence of software health metrics, and clear information on how to contact developers seem to be associated with increased usage rates. Our findings can help scientific software developers make the most out of evaluations of their software.