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    Because highly reliable software is becoming an essential ingredient in many systems, software developers apply various techniques to discover faults early in development, such as more rigorous reviews, more extensive testing, and strategic assignment of key personnel. Our goal is to target reliability enhancement activities to those modules that are most likely to have problems. This paper presents a methodology that incorporates genetic programming for predicting the order of software modules based on the expected number of faults. This is the first application of genetic programming to software engineering that we know of. We found that genetic programming can be used to generate software quality models whose inputs are software metrics collected earlier in development, and whose output is a prediction of the number of faults that will be discovered later in development or during operations. We established ordinal evaluation criteria for models, and conducted an industrial case study of software from a military communications system. Case study results were sufficiently good to be useful to a project for choosing modules for extra reliability enhancement treatment.