Abstract Estimating the number of defects in a software product is an important and challenging problem. A multitude of estimation techniques have been proposed for defect prediction. However, not all techniques are applicable in all cases. The selection of the proper approach to use depends on multiple factors: the features of the approach; the availability of resources; and the goals for using the estimated defect data. In this paper a survey of existing estimation techniques and a decision support approach for selecting the most suitable defect estimation technique for a project, with specific goals, is proposed. The results of the ranking are a clear indication that no estimation technique provides a single, comprehensive solution; the selection must be done according to a given scenario. Copyright © 2009 John Wiley & Sons, Ltd.

An accurate prediction of the number of defects in a software product during system testing contributes not only to the management of the system testing process but also to the estimation of the product's required maintenance. Here, a new approach called ED3M is presented that computes an estimate of the total number of defects in an ongoing testing process. ED3M is based on estimation theory. Unlike many existing approaches the technique presented here does not depend on historical data from previous projects or any assumptions about the requirements and/or testers' productivity. It is a completely automated approach that relies only on the data collected during an ongoing testing process. This is a key advantage of the ED3M approach, as it makes it widely applicable in different testing environments. Here, the ED3M approach has been evaluated using five data sets from large industrial projects and two data sets from the literature. In addition, a performance analysis has been conducted using simulated data sets to explore its behavior using different models for the input data. The results are very promising; they indicate the ED3M approach provides accurate estimates with as fast or better convergence time in comparison to well-known alternative techniques, while only using defect data as the input.