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Predicting the number of faults is not always necessary to guide quality development; it may be enough to identify the most troublesome modules. Predicting the quality of modules lets developers focus on potential 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 applies discriminant analysis to identify fault-prone modules in a sample representing about 1.3 million lines of code from a very large telecommunications system. We developed two models using design product metrics based on call graphs and control flow graphs. One model used only these metrics; the other included reuse information as well. Both models had excellent fit. However, the model that included reuse data had substantially better predictive accuracy. We thus learned that information about reuse can be a significant input to software quality models for improving the accuracy of predictions.
Kalai S. Kalaichelvan, Edward B. Allen, Nishith Goel, Taghi M. Khoshgoftaar, "Early Quality Prediction: A Case Study in Telecommunications", IEEE Software, vol. 13, no. , pp. 65-71, January 1996, doi:10.1109/52.476287
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