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A number of papers have investigated the relationships between design metrics and the detection of faults in object-oriented software. Several of these studies have shown that such models can be accurate in predicting faulty classes within one particular software product. In practice, how-ever, 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? Or-ganizations typically learn and change. From a more gen-eral standpoint, can we obtain any evidence that such mod-els 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 mid-size Java systems developed in the same environment. An-other contribution of the paper is the use of a novel explora-tory analysis technique (MARS) to build such fault-proneness models, whose functional form is a priori un-known.