Software quality control and prediction model for maintenance

Abstract

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 by using Boolean discriminant functions. The model also predicts during development the quality that will be delivered to maintenance by using both point and confidence interval estimates of quality. 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. Related to this property of dominance is the property of concordance, which is the degree to which a set of metrics produces the same result in classifying software quality. A high value of concordance implies that additional metrics will not make a significant contribution to accurately classifying quality; hence, these metrics are redundant. Data from the Space Shuttle flight software are used to illustrate the model process.