Application of Association Rules Mining to Analysis of Risk Factors in Software Development Projects

Abstract

In this paper, we characterize runaway projects using risk factors in software projects. At first, we applied association rule mining technique to questionnaire data collected from actual software development. Then as the result of the association rule mining, we derived several characterizing rules on risk factors, which will enable us to predict runaway status of projects. Finally we evaluated the usefulness of the derived rules by comparing with the prediction model constructed by logistic regression analysis. The result of evaluation implies that both the derived rules and their related risk factors are effective to characterize runaway projects.