Methodology for Validating Software Metrics
IEEE Transactions on Software Engineering
Predicting Fault-Prone Software Modules in Telephone Switches
IEEE Transactions on Software Engineering
Detection of Fault-Prone Software Modules During a Spiral Life Cycle
ICSM '96 Proceedings of the 1996 International Conference on Software Maintenance
Software Metrics Model For Quality Control
METRICS '97 Proceedings of the 4th International Symposium on Software Metrics
Software Metrics Model For Integrating Quality Control And Prediction
ISSRE '97 Proceedings of the Eighth International Symposium on Software Reliability Engineering
Predicting Fault-Prone Classes with Design Measures in Object-Oriented Systems
ISSRE '98 Proceedings of the The Ninth International Symposium on Software Reliability Engineering
Predicting the Order of Fault-Prone Modules in Legacy Software
ISSRE '98 Proceedings of the The Ninth International Symposium on Software Reliability Engineering
Determining Fault Insertion Rates for Evolving Software Systems
ISSRE '98 Proceedings of the The Ninth International Symposium on Software Reliability Engineering
Software Reliability and Maintenance Concept Used for Automatic Call Distributor MEDIO ACD
ISSRE '00 Proceedings of the 11th International Symposium on Software Reliability Engineering
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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.