Abstract
A metrics validation process is defined that integrates quality factors and quality functions. It proposes a comprehensive metric validation methodology that has validity criteria, which support the quality function and activities conducted by software organization for the purpose of achieving project quality goals. In this paper, valid metrics are assessing differences in quality, assessing relative quality, control quality (discrimination between high quality and low quality), control quality (tracking changes), and prediction quality. The criteria are defined and illustrated by association, consistency, discriminative power, tracking. Statistical methods such as Mann-Whitency, Wilcoxon Rank Sum test, Wald-Wolfowitz, and Discriminate Analysis play an important role in evaluating metrics against the validity criterion.
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© 2005 Springer-Verlag Berlin Heidelberg
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Lee, MC. (2005). Statistical Data Analysis for Software Metrics Validation. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3684. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11554028_54
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DOI: https://doi.org/10.1007/11554028_54
Publisher Name: Springer, Berlin, Heidelberg
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