Predicting deviations in software quality by using relative critical value deviation metrics

摘要

We develop a new metric, relative critical value deviation (RCVD), for classifying and predicting software quality. The RCVD is based on the concept that the extent to which a metric's value deviates from its critical value, normalized by the scale of the metric, indicates the degree to which the item being measured does not conform to a specified norm. For example, the deviation in body temperature above 98.6 Fahrenheit degrees is a surrogate for fever. Similarly, the RCVD is a surrogate for the extent to which the quality of software deviates from acceptable norms (e.g., zero discrepancy reports). Early in development, surrogate metrics are needed to make predictions of quality before quality data are available. The RCVD can be computed for a single metric or multiple metrics. Its application is in assessing newly developed modules by their quality in the absence of quality data. The RCVD is a part of the larger framework of our measurement models that include the use of Boolean discriminant functions for classifying software quality. We demonstrate our concepts using Space Shuttle flight software data.