Using classification trees for software quality models: lessons learned

Abstract

High software reliability is an important attribute of high-assurance systems. Software quality models yield timely predictions of reliability indicators on a module-by-module basis, enabling one to focus on finding faults early in development. This paper introduces the CART (Classification And Regression Trees) algorithm to practitioners in high-assurance systems engineering. This paper presents practical lessons learned in building classification trees for software quality modeling, including an innovative way to control the balance between misclassification rates. A case study of a very large telecommunications system used CART to build software quality models. The models predicted whether or not modules would have faults discovered by customers, based on various sets of software product and process metrics as independent variables. We found that a model based on two software product metrics had an accuracy that was comparable to a model based on 40 product and process metrics.