  As software systems have grown in scale and complexity the test suites built
alongside those systems have also become increasingly complex. Understanding
key aspects of test suites, such as their coverage of production code, is
important when maintaining or reengineering systems. This work investigates the
distribution of unit tests in Open Source Software (OSS) systems through the
visualization of data obtained from both dynamic and static analysis. Our
long-term aim is to support developers in their understanding of test
distribution and the relationship of tests to production code. We first obtain
dynamic coupling information from five selected OSS systems and we then map the
test and production code results. The mapping is shown in graphs that depict
both the dependencies between classes and static test information. We analyze
these graphs using Centrality metrics derived from graph theory and SNA. Our
findings suggest that, for these five systems at least, unit test and dynamic
coupling information 'do not match', in that unit tests do not appear to be
distributed in line with the systems' dynamic coupling. We contend that, by
mapping dynamic coupling data onto unit test information, and through the use
of software metrics and visualization, we can locate central system classes and
identify to which classes unit testing effort has (or has not) been dedicated.
