Multiscale Visualization of Dynamic Software Logs

Abstract

We present a set of techniques and design principles for the visualization of large dynamic software logs consisting of attributed change events, such as obtained from instrumenting programs or mining software repositories. We enhance the visualization scalability with importance-based antialiasing techniques that guarantee visibility of several types of events. We present a hierarchical clustering method that uncovers several patterns of interest in the event logs, such as same-lifetime memory allocations and software releases. We visualize the clusters using a new type of technique called interleaved cushions. We demonstrate our methods on two real-world problems: the monitoring of a dynamic memory allocator and the analysis of a software repository.