Predicting Fault-Proneness of Reused Object-Oriented Classes in Software Post-Releases

The performance and applicability of a fault-proneness prediction model built based on internal quality measures computed on classes of one or more software systems and applied to classes from a different software system may be heavily influenced by several factors. These factors are related to the specific characteristics of the software system and its development environment. The issues related to these factors may be alleviated by building a model on a release of a software system and then applying it to following releases, in which product and process characteristics are close to the initial release. In this paper, we investigate the ability of the fault-proneness prediction models built by using fault data available up to a release of a system to predict faulty classes among those that are potentially reusable and those that are actually reused in the subsequent releases. We adapt an approach to building and using prediction models for classes reused, with or without modification, from preceding releases of a system. Our results show that relying on up-to-date fault data for the classes of a software system may significantly improve the overall accuracy in predicting fault-proneness of the post-release classes reused from an initial release. A model based on data from a system may be applied to other systems with a lesser degree of accuracy than a model built on up-to-date data from the same system.