Classification techniques for metric-based software development

Abstract

Managing software development and maintenance projects requires predictions about components of the software system that are likely to have a high error rate or that need high development effort. The value of any classification is determined by the accuracy and cost of such predictions. The paper investigates the hypothesis whether fuzzy classification applied to criticality prediction provides better results than other classification techniques that have been introduced in this area. Five techniques for identifying error-prone software components are compared, namely Pareto classification, crisp classification trees, factor-based discriminant analysis, neural networks, and fuzzy classification. The comparison is illustrated with experimental results from the development of industrial real-time projects. A module quality model — with respect to changes — provides both quality of fit (according to past data) and predictive accuracy (according to ongoing projects). Fuzzy classification showed best results in terms of overall predictive accuracy.