Assessing the Applicability of Fault-Proneness Models Across Object-Oriented Software Projects

A number of papers have investigated the relationships between design metrics and the detection of faults in objectoriented software. Several of these studies have shown that such models can be accurate in predicting faulty classes within one particular software product. In practice, however, prediction models are built on certain products to be used on subsequent software development projects. How accurate can these models be considering the inevitable differences that may exist across projects and systems? Organizations typically learn and change. From a more general standpoint, can we obtain any evidence that such mo dels are economically viable tools to focus validation and verification effort? This paper attempts to answer these questions using fault and design data collected on two midsize Java systems developed in the same environment. Another contribution of the paper is the use of a novel exploratory analysis technique (MARS) to build such faultproneness models, whose functional form is a priori unknown. Results indicate that a model built on one system can be accurately used to rank classes within another system according to their fault-proneness. The downside, however, is that, because of system differences, the predicted fault probabilities are not representative of the system predicted. Furthermore, a cost-benefit model demonstrates that the MARS fault-proneness model is potentially viable, from an economical standpoint. The linear model is not, thus suggesting a more complex model is required.