Scaling an object-oriented system execution visualizer through sampling

Abstract

Increasingly, applications are being built by combining existing software components. For the most part, a software developer can treat the components as black-boxes. However, for some tasks, such as when performance tuning, a developer must consider how the components are implemented and how they interact. In these cases, a developer may be able to perform the task more effectively by using dynamic information about how the system executes. In previous work, we demonstrated the utility of a tool, called AVID (Architectural VIsualization of Dynamics), that animates dynamic information in terms of developer-chosen architectural views. One limitation of this earlier work was that AVID relied on trace information collected about the system's execution; traces for even small parts of a system's execution can be enormous, limiting the duration of execution that can be considered. To enable AVID to scale to larger, longer-running systems, we have been investigating the visualization and animation of sampled dynamic information. In this paper, we discuss the addition of sampling support to AVID, and we present two case studies in which we experimented with animating sampled dynamic information to help with performance tuning tasks.

Automatic generation of abstract views for legacy software comprehension

“…Very few authors have worked on sampling or segmentation techniques for trace analysis. One pioneering work is the one of Chan et al [9] to visualize long sequence of low-level Java execution traces in the AVID system (including memory event and call stack events). But their approach is quite different from ours.…”

Execution trace analysis through massive sequence and circular bundle views

“…AVID, a visualization tool by Walker et al [33,4], aims at exploring a system's behavior by manually defining a high-level model of a system and then enriching it with trace data collected during the system's execution. This is a manual step that involves multiple iterations, thus incrementally improving the user's comprehension of the system.…”

Computing dynamic clusters

“…Very few authors have worked on sampling or segmentation techniques for trace analysis. One pioneering work is the one of Chan et al [24] to visualize long sequence of low-level Java execution traces in the AVID system (including memory event and call stack events). But their approach is quite different from ours.…”