Program comprehension using dynamic information is one of key tasks of software maintenance. Software visualization with sequence diagrams is a promising technique to help developer comprehend the behavior of object-oriented systems effectively. There are many tools that can support automatic generation of a sequence diagram from execution traces. However it is still difficult to understand the behavior because the size of automatically generated sequence diagrams from the massive amounts of execution traces tends to be beyond developer's capacity. In this paper, we propose an execution trace slicing and visualization method. Our proposed method is capable of slice calculation based on a behavior model which can treat dependencies based on static and dynamic analysis and supports for various programs including exceptions and multi-threading. We also introduce our tool that perform our proposed slice calculation on the Eclipse platform. We show the applicability of our proposed method by applying the tool to two Java programs as case studies. As a result, we confirm effectiveness of our proposed method for understanding the behavior of object-oriented systems.

Behaviors of an object-oriented system can be visualized as reverse-engineered sequence diagrams from execution traces. This approach is a valuable tool for program comprehension tasks. However, owing to the massiveness of information contained in an execution trace, a reverse-engineered sequence diagram is often afflicted by a scalability issue. To address this issue, many trace summarization techniques have been proposed. Most of the previous techniques focused on reducing the vertical size of the diagram. To cope with the scalability issue, decreasing the horizontal size of the diagram is also very important. Nonetheless, few studies have addressed this point; thus, there is a lot of needs for further development of horizontal summarization techniques. We present in this paper a method for identifying core objects for trace summarization by analyzing reference relations and dynamic properties. Visualizing only interactions related to core objects, we can obtain a horizontally compactified reverse-engineered sequence diagram that contains system's key behaviors. To identify core objects, first, we detect and eliminate temporary objects that are trivial for a system by analyzing reference relations and lifetimes of objects. Then, estimating the importance of each non-trivial object based on their dynamic properties, we identify highly important ones (i.e., core objects). We implemented our technique in our tool and evaluated it by using traces from various open-source software systems. The results showed that our technique was much more effective in terms of the horizontal reduction of a reverse-engineered sequence diagram, compared with the state-of-the-art trace summarization technique. The horizontal compression ratio of our technique was 134.6 on average, whereas that of the state-of-the-art technique was 11.5. The runtime overhead imposed by our technique was 167.6% on average. This overhead is relatively small compared with recent scalable dynamic analysis techniques, which shows the practicality of our technique. Overall, our technique can achieve a significant reduction of the horizontal size of a reverse-engineered sequence diagram with a small overhead and is expected to be a valuable tool for program comprehension.