Balancing Misclassification Rates in Classification-TreeModels of Software Quality
Empirical Software Engineering
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We develop a quality control and prediction model for improving the quality of software delivered by development to maintenance. This model identifies modules that require priority attention during development and maintenance. The model also predicts during development the quality that will be delivered to maintenance. We show that it is important to perform a marginal analysis when making a decision about how many metrics to include in a discriminant function. If many metrics are added at once, the contribution of individual metrics is obscured. Also, the marginal analysis provides an effective rule for deciding when to stop adding metrics. We also show that certain metrics are dominant in their effects on classifying quality and that additional metrics are not needed to increase the accuracy of classification. Data from the Space Shuttle flight software are used to illustrate the model process.