Date Added: Oct 2009
Software metrics offer one the promise of distilling useful information from vast amounts of software in order to track development progress, to gain insights into the nature of the software, and to identify potential problems. Unfortunately, however, many software metrics exhibit highly skewed, non-Gaussian distributions. As a consequence, usual ways of interpreting these metrics - for example, in terms of "Average" values - can be highly misleading. Many metrics, it turns out, are distributed like wealth - with high concentrations of values in selected locations. The authors propose to analyze software metrics using the Gini coefficient, a higher-order statistic widely used in economics to study the distribution of wealth.