Abstract-We propose a comprehensive metrics validation methodology that has six validity criteria, which support the quality functions assessment, control, and prediction, where quality functions are activities conducted by software organizations for the purpose of achieving project quality goals. Six criteria are defined and illustrated: association, consistency, discriminative power, tracking, predictability, and repeatability. We show that nonparametric statistical methods such as contingency tables play an important role in evaluating metrics against the validity criteria. Examples emphasizing the discriminative power validity criterion are presented. A metrics validation process is defined that integrates quality factors, metrics, and quality functions.

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A large number of software product metrics have been proposed in software engineering. We use the term metric here to be consistent with prevailing international standards. Product metrics quantitatively characterize some aspect of the structure of a software product, such as a requirements specification, a design, or source code. They are also commonly collectively known as complexity metrics . While many of these metrics are based on good ideas about what is important to measure in software to capture its complexity, it is still necessary to systematically validate them. Two types of validation are recognized internal and external. Internal validation is a theoretical exercise that ensures that the metric is a proper numerical characterization of the property it claims to measure. Demonstrating that a metric measures what it purports to measure is a form of theoretical validation. Typically, one defines the properties of the attribute that is to be measured, for example, the properties of module coupling. Then one demonstrates analytically that the product metric satisfies these properties. External validation involves empirically demonstrating that the product nature is associated which is important external metter or an improvement is shown above. Internal and external validation are also commonly referred to as theoretical and empirical validation respectively This article presents a comprehensive methodology for validating software product metrics. Our focus will be limited to empirical validation.