Context-driven Software Project Estimation
This addresses the need for more accurate software project estimation by incorporating context, but it appears incremental as it builds on existing clustering methods.
The paper tackles the problem of software project estimation by introducing the SPRINT I technique, which uses context knowledge to characterize clusters of similar past projects for prediction, with initial evaluations conducted.
Using quantitative data from past projects for software project estimation requires context knowledge that characterizes its origin and indicates its applicability for future use. This article sketches the SPRINT I technique for project planning and controlling. The underlying prediction mechanism is based on the identification of similar past projects and the building of so-called clusters with typical data curves. The article focuses on how to characterize these clusters with context knowledge and how to use context information from actual projects for prediction. The SPRINT approach is tool-supported and first evaluations have been conducted.