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Software maintenance activities require a sufficient level of understanding of the software at hand that unfortunately is not always readily available. Execution trace visualization is a common approach in gaining this understanding, and among our own efforts in this context is EXTRAVIS, a tool for the visualization of large traces. While many such tools have been evaluated through case studies, there have been no quantitative evaluations to the present day. This paper reports on the first controlled experiment to quantitatively measure the added value of trace visualization for program comprehension.We designed eight typical tasks aimed at gaining an understanding of a representative subject system, and measured how a control group (using the Eclipse IDE) and an experimental group (using both Eclipse and EXTRAVIS) performed in terms of correctness and time spent. The results are statistically significant in both regards, showing a 22% decrease in time needed for the given tasks, and a 43% increase in correctness of the results for the group using trace visualization. Index TermsProgram comprehension, dynamic analysis, controlled experiment.B. Cornelissen is with the Software Improvement Group, A.J.

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The use of dynamic information to aid in software understanding is a common practice nowadays. One of the many approaches concerns the comprehension of execution traces. A major issue in this context is scalability: due to the vast amounts of information, it is a very difficult task to successfully find your way through such traces without getting lost. In this paper, we propose the use of a novel trace visualization method based on a massive sequence and circular bundle view, constructed with scalability in mind. By means of three usage scenarios that were conducted on three different software systems, we show how our approach, implemented in a tool called EXTRAVIS, is applicable to the areas of trace exploration, feature location, and feature comprehension.

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