Control flow graph visualization and its application to coverage and fault localization in Python




Salling, Jackson Lee

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This report presents a software testing tool that creates visualizations of the Control Flow Graph (CFG) from Python source code. The CFG is a representation of a program that shows execution paths that may be taken by the machine. Similar techniques to the ones here could be applied to many other languages, but the CFGs in this tool are tailored to the Python language. As computers get faster, tools to help programmers be effective at work can become more complex and still give quick feedback, without causing an undue performance burden. This tool explores several approaches to giving feedback to developers through a visualization of the CFG. First, just the viewing of a CFG gives a different perspective on the code. A programmer could choose to juxtapose the CFG with complexity metrics during development, seeing increased complexity as graphs grow larger. Second, the tool implements a mechanism to provide code coverage to Python modules. This feature extends the visualization to show code coverage as a highlighted CFG. Test coverage requirements are calculated to check node, edge, edge-pair, and prime path coverage. From studying existing testing tools, it appears no existing tool for Python provides all these test coverage levels. Third, the tool provides an interface for adding custom highlighting of the CFG, used here to visualize fault localization. Seeing the most suspicious locations from fault localization techniques could be used to reduce debugging time. The results of running the tool on several popular Python packages, and on itself, show its performance is competitive with the most popular coverage tool when measuring branch coverage. It is slightly slower on statement cover- age alone, but much faster against an unoptimized version and a logic coverage tool. This report also presents ideas for extensions to the tool. Among them is to incorporate program repair using fault localization and mutation operators. Visualizing code as a CFG provides interesting ways to look at many software testing metrics.



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