Collaboration in Scientific Software: A Systematic Literature Review

The development and use of scientific software often entails collaborative work within and across teams. Understanding how such teams collaborate in addition to other factors that influence their likelihood of success is critical given the prevalence and necessity of teamwork for scientific software [1]. In particular, we contend that understanding the current state of collaboration in scientific software projects affords the discovery of opportunities to design more effective collaborations and produce better software and scientific outcomes. To advance the study of teamwork for scientific software, we conducted a systematic literature review (SLR) with a focus on papers that analyze or otherwise describe collaborative work in the domain of scientific software [2]. The main objective of the SLR was thus to provide a foundation for future research based on key insights and gaps identified in the literature on scientific software teams. Our search was applied to three databases (Web of Science, ACM Digital Library, IEEE Xplore) with backward and forward citation search applied to papers selected for final analysis [3]. The results of the SLR indicate that teamwork in scientific software remains understudied: collaboration is repeatedly mentioned but rarely the focus of investigation. At the same time, researchers in this domain recognize the significance of teamwork and have sought to develop tools with the goal of facilitating various aspects of collaboration like communication and data sharing. The evaluation of collaboration tools and the collaboration itself is generally not reported in these publications, suggesting that such evaluations are infrequent, undervalued, and/or constrained (e.g., due to lack of time and funding). Fitting with recent calls for research [4], our results highlight the need for a concerted effort to analyze the inputs, processes, and outcomes of collaborative work in the development and use of scientific software. We propose a path forward for studies of collaboration in scientific software aimed at enhancing both teamwork and software, with emphasis on team roles, cross-disciplinary requirements, and generative AI.