Comparative study of ordering and classification of fault-prone software modules

Software quality models can predict the quality of modules early enough for cost-effective prevention of problems. For example, software product and process metrics can be the basis for predicting reliability. Predicting the exact number of faults is often not necessary; classification models can identify fault-prone modules. However, such models require that 'fault-prone' be defined before modeling, usually via a threshold. This may not be practical due to uncertain limits on the amount of reliability-improvement effort. In such cases, predicting the rank-order of modules is more useful. A module-order model predicts the rank-order of modules according to a quantitative quality factor, such as the number of faults. This paper demonstrates how module-order models can be used for classification, and compares them with statistical classification models. Two case studies of full-scale industrial software systems compared nonparametric discriminant analysis with module-order models. One case study examined a military command, control, and communications system. The other studied a large legacy telecommunications system. We found that module-order models give management more flexible reliability enhancement strategies than classification models, and in these case studies, yielded more accurate results than corresponding discriminant models.