Object Oriented-Based Metrics to Predict Fault Proneness in Software Design

TL;DR

The paper addresses predicting software fault proneness from object-oriented design metrics. It conducts a literature-dimension analysis of prior empirical studies, focusing on CK metrics and related OO metrics to identify effective predictors. Key findings indicate that coupling, complexity, and inheritance-related metrics are frequently associated with fault proneness, though predictive power varies by dataset and method, leading to some contradictory results. The work underscores the importance of metric selection tailored to the dataset and context, with practical implications for early fault detection and design-stage quality assurance.

Abstract

In object-oriented software design, various metrics predict software systems' fault proneness. Fault predictions can considerably improve the quality of the development process and the software product. In this paper, we look at the relationship between object-oriented software metrics and their implications on fault proneness. Such relationships can help determine metrics that help determine software faults. Studies indicate that object-oriented metrics are indeed a good predictor of software fault proneness, however, there are some differences among existing work as to which metric is most apt for predicting software faults.