Context-driven Software Project Estimation

TL;DR

This paper presents the SPRINT I technique for software project estimation, which uses context-aware clustering of past projects to improve prediction accuracy, supported by a tool and initial evaluations.

Contribution

It introduces a novel context-driven clustering method for project estimation and demonstrates its application through a tool and preliminary assessments.

Findings

Effective clustering of past projects improves estimation accuracy

Context knowledge enhances the relevance of project data

Initial evaluations show promising results for the approach

Abstract

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.