Investigating effect of Design Metrics on Fault Proneness in Object-Oriented Systems

Demand for quality software has undergone with rapid growth during the last few years. This is leading to an increase in the development of metrics for measuring the properties of software such as coupling, cohesion or inheritance that can be used in early quality assessments. Quality models that explore the relationship between these properties and quality attributes such as fault proneness, maintainability, effort or productivity are needed to use these metrics effectively. The goal of this work is to empirically explore the relationship between object-oriented design metrics and fault proneness of object-oriented system classes. The study used data collected from Java applications is containing 136 classes. We use a set of twenty-six design metrics in our work. Result of this study shows that many metrics are based on comparable ideas and provide redundant information. It is shown that by using a subset of metrics in the prediction models can be built to identify the faulty classes. The proposed model predicts faulty classes with more than 80% accuracy.