Results are presented of an analysis of several defect models using data collected from two large commercial projects. Traditional models typically use either program matrices (i.e. measurements from software products) or testing time or combinations of these as independent variables. The limitations of such models have been well-documented. The models considered use the number of defects detected in the earlier phases of the development process as the independent variable. This number can be used to predict the number of defects to be detected later, even in modified software products. A strong correlation between the number of earlier defects and that of later ones was found. Using this relationship, a mathematical model was derived which may be used to estimate the number of defects remaining in software. This defect model may also be used to guide software developers in evaluating the effectiveness of the software development and testing processes.>

A major portion of the effort expended in developing commercial software today is associated with program testing. Schedule and/ or resource constraints frequently require that testing be conducted so as to uncover the greatest number of errors possible in the time allowed. In this paper we describe a study undertaken to assess the potential usefulness of various product-and process-related measures in identifying error-prone software. Our goal was to establish an empirical basis for the efficient utilization of limited testing resources using objective, measurable criteria. Through a detailed analysis of three software products and their error discovery histories, we have found simple metrics related to the amount of data and the structural complexity of programs to be of value for this purpose.