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Estimating software fault-proneness early, i.e., predicting the probability of software modules to be faulty, can help in reducing costs and increasing effectiveness of software analysis and testing. The many available static metrics provide important information, but none of them can be deterministically related to software fault-proneness. Fault-proneness models seem to be an interesting alternative, but the work on these is still biased by lack of experimental validation.

This paper discusses barriers and problems in using software fault-proneness in industrial environments, proposes a method for building software fault-proneness models based on logistic regression and cross-validation that meets industrial needs, and provides some experimental evidence of the validity of the proposed approach.