Predicting Quality of Object-Oriented Systems through a Quality Model Based on Design Metrics and Data Mining Techniques

Abstract

Most of the existing object-oriented design metrics and data mining techniques capture similar dimensions in the data sets, thus reflecting the fact that many of the metrics are based on similar hypotheses, properties, and principles. Accurate quality models can be built to predict the quality of object-oriented systems by using a subset of the existing object-oriented design metrics and data mining techniques. We propose a software quality model, namely QUAMO (QUAlity MOdel) which is based on divide-and-conquer strategy to measure the quality of object-oriented systems through a set of object-oriented design metrics and data mining techniques. The primary objective of the model is to make similar studies on software quality more comparable and repeatable. The proposed model is augmented from five quality models, namely McCall Model, Boehm Model, FURPS/FURPS+ (i.e. functionality, usability, reliability, performance, and supportability), ISO 9126, and Dromey Model. We empirically evaluated the proposed model on several versions of JUnit releases. We also used linear regression to formulate a prediction equation. The technique is useful to help us interpret the results and to facilitate comparisons of results from future similar studies.