Abstract
A significant challenge in software engineering is accurately modeling projects in order to correctly forecast success or failure. The primary difficulty is that software development efforts are complex in terms of both the technical and social aspects of the engineering environment. This is compounded by the lack of real data that captures both the measures of success in performing a process, and the measures that reflect a group�s social dynamics. This research focuses on the development of a model for predicting software project success that leverages the wealth of available open source project data in order to accurately model the behavior of those software engineering groups. Our model accounts for both the technical elements of software engineering as well as the social elements that drive the decisions of individual developers. We use agent-based simulations to represent the complexity of the group interactions, and base the behavior of the agents on the real software engineering data acquired. For four of the five project success measures, our results indicate that the developed model represents the underlying data well and provides accurate predictions of open source project success indicators.
