Software measurement data analysis using memory-based reasoning

Abstract

The goal of accurate software measurement data analysis is to increase the understanding and improvement of software development process together with increased product quality and reliability. Several techniques have been proposed to enhance the reliability prediction of software systems using the stored measurement data, but no single method has proved to be completely effective. One of the critical parameters for software prediction systems is the size of the measurement data set, with large data sets providing better reliability estimates. In this paper, we propose a software defect classification method that allows defect data from multiple projects and multiple independent vendors to be combined together to obtain large data sets. We also show that once a sufficient amount of information has been collected, the memory-based reasoning technique can be applied to projects that are not in the analysis set to predict their reliabilities and guide their testing process. Finally, the result of applying this approach to the analysis of defect data generated from fault-injection simulation is presented.