A Novel Approach Based on Gestalt Psychology for Abstracting the Content of Large Execution Traces for Program Comprehension

The analysis of execution traces can reveal important information about the behavioral aspects of complex software systems, hence reducing the time and effort it takes to understand and maintain them. Traces, however, tend to be considerably large which hinders their effective analysis. Existing traces analysis tools rely on some sort of visualization techniques to help software engineers make sense of trace content. Many of these techniques have been studied and found to be limited in many ways. In this paper, we present a novel trace analysis technique that automatically divides the content of a large trace into meaningful segments that correspond to the program's main execution phases such as initializing variables, performing a specific computation, etc. These phases can simplify significantly the exploration of large traces by allowing software engineers to first understand the content of a trace at a high-level before they decide to dig into the details. Our phase detection method is inspired by Gestalt laws that characterize the proximity, similarity, and continuity of the elements of a data space. We model these concepts in the context of execution traces and show how they can be used as gravitational forces that yield the formation of dense groups of trace elements, which indicate candidate phases. We applied our approach to two software systems. The results are very promising.