EARLY QUALITY PREDICTION - A CASE-STUDY IN TELECOMMUNICATIONS

Predicting the number of faults is not always necessary to guide quali ty development; it may be enough to identify the most troublesome modu les. Predicting the quality of modules lets developers focus on potent ial problems and make improvements earlier in development, when it is more cost-effective. In such cases, classification models rather than regression models work very well. As a case study, this article applie s discriminant analysis to identify fault-prone modules in a sample re presenting about 1.3 million lines of code from a very large telecommu nications system. We developed two models using design product metrics based on call graphs and control-flow graphs. One model used only the se metrics; the other included reuse information as well. Both models had excellent fit. However, the model that included reuse data had sub stantially better predictive accuracy. We thus learned that informatio n about reuse can be a significant input to software quality models fo r improving the accuracy of predictions.