Software metrics model for integrating quality control and prediction

A model is developed that is used to validate and apply metrics for quality control and quality prediction, with the objective of using metrics as early indicators of software quality problems. Metrics and quality factor data from the Space Shuttle flight software are used as an example. Our approach is to integrate quality control and prediction in a single model and to validate metrics with respect to a quality factor. Boolean discriminant functions (BDFs) were developed for use in the quality control and quality prediction process. BDFs provide good accuracy for classifying low quality software because they include additional information for discriminating quality: critical values. Critical values are threshold values of metrics that are used to either accept or reject modules when the modules are inspected during the quality control process. A series of nonparametric statistical methods is also used in the method presented. It is important to perform a marginal analysis when making a decision about how many metrics to use in the quality control and prediction process. We found that certain metrics are dominant in their effects on classifying quality and that additional metrics are not needed to accurately classify quality. This effect is called dominance. Related to the 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.