A NEURAL-NETWORK APPROACH FOR EARLY DETECTION OF PROGRAM MODULES HAVING HIGH-RISK IN THE MAINTENANCE PHASE

A neural network model is developed to classify program modules as either high or low risk based on multiple criterion variables. The inputs to the model include a selection of software complexity metrics collected from a telecommunications system. Two criterion variables are used for class determination: the number of changes to enhance the program modules, and the number of changes required to remove faults from the modules. The data were deliberately biased to magnify differences in metrics values between the discriminant groups. The technique displayed a low classification error rate. This success, and the absence of the data assumptions typical of statistical techniques, demonstrate the utility of neural networks in isolating high-risk modules where class determination is based on multiple quality metrics.