Combining Dynamic Analysis and Visualization to Explore the Distribution of Unit Test Suites

Abstract

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.

A Large-Scale Study on the Usage of Testing Patterns That Address Maintainability Attributes: Patterns for Ease of Modification, Diagnoses, and Comprehension

“…A number of works have been published which study the relationships between production code and unit tests. Using graphs obtained from static and dynamic analysis, Tahir and MacDonell [27] applied centrality measures to quantify the distribution of unit tests across five open source projects in order to identify the parts of those projects where the most testing effort was focused. Zaidman et.…”

“…Van Rompaey and Demeyer [29] have established conventions for improving the traceability of test code such as naming conventions, static call graphs, and lexical analysis. These methods have been used in other papers to [27] establish heuristics for identifying tests in source code.…”