Requirements Engineering in Building Climate Science Software.

Abstract

Software has an important role in supporting scientific work. This dissertation studies teams that build scientific software, focusing on the way that they determine what the software should do. These requirements engineering processes are investigated through three case studies of climate science software projects. The Earth System Modeling Framework assists modeling applications, the Earth System Grid distributes data via a web portal, and the NCAR (National Center for Atmospheric Research) Command Language is used to convert, analyze and visualize data. Document analysis, observation, and interviews were used to investigate the requirements-related work.

The first research question is about how and why stakeholders engage in a project, and what they do for the project. Two key findings arise. First, user counts are a vital measure of project success, which makes adoption important and makes counting tricky and political. Second, despite the importance of quantities of users, a few particular “power users” develop a relationship with the software developers and play a special role in providing feedback to the software team and integrating the system into user practice.

The second research question focuses on how project objectives are articulated and how they are put into practice. The team seeks to both build a software system according to product requirements but also to conduct their work according to process requirements such as user support. Support provides essential communication between users and developers that assists with refining and identifying requirements for the software. It also helps users to learn and apply the software to their real needs. User support is a vital activity for scientific software teams aspiring to create infrastructure.

The third research question is about how change in scientific practice and knowledge leads to changes in the software, and vice versa. The “thickness” of a layer of software infrastructure impacts whether the software team or users have control and responsibility for making changes in response to new scientific ideas. Thick infrastructure provides more functionality for users, but gives them less control of it. The stability of infrastructure trades off against the responsiveness that the infrastructure can have to user needs.