Building knowledge discovery-driven models for decision support in project management

摘要

Accurate estimations of software size in the early stages of a software project are critical in software project management because they lead to a good planning and reduce project costs. In this work, the relation between early software size measures as the function points and measures of the final product as the lines of code has been studied. A process to refine association rules, based on the generation of unexpected patterns, is proposed. The goal is to generate strong association rules between attributes that can be obtained early in the project and the final software size.