EAGER: Ecosystem Modeling for Infrastructure Evaluation

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This research investigates how scientific software developers, maintainers, users, and funders interact, with the goal of maximizing the effectiveness of funds for scientific software development. The interactions among these groups are modeled and analyzed through techniques used to study ecosystems. The ecosystem analogy is being used successfully to describe the relationships among interacting entities particularly when organizations may collaborate on some activities and compete in others. These techniques support the representation and analysis of large numbers of artifacts such as the thousands of individual files integrated to form a complete software package. The relationships are diverse, so a basic graph representation is used to model the interactions. Then, a variety of algorithms are used to compute graph attributes such as betweenness centrality, from which characteristics of the relationships can be derived. This information can be used to determine certain qualities of the software, the diversity of its use, and the amount of code reused from other projects and libraries. This research will make fundamental contributions to socio-technical ecosystem methods in the form of new techniques for making decisions about the viability of an ecosystem. The results of this research will help development managers and funding managers make effective decisions by addressing long-term viability and sustainment issues. This includes the appropriateness of specific software modules and packages for use in scientific infrastructures. New techniques for evaluating software for adoption by an organization for use or inclusion in their own products will be developed. By taking the comprehensive ecosystem approach and using graph-based quality metrics, an organization will be able to more accurately estimate the effort that will be required to initially renovate, and eventually maintain, a software component. The results will demonstrate how the developed techniques can help to manage risk in a science project, and how these techniques will result in more efficient use of resources, including federal funding.