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.

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The analysis of execution traces can be useful in many software engineering activities including debugging, feature enhancement, performance analysis, and any other task that requires some degree of understanding of the way a system behaves. Traces, however, tend to be considerably large, which often hinders effective analysis of their content.There is a need to investigate ways to help software engineers find and understand important information conveyed in a trace despite the trace being massive. Motivated by the work done in the area of text mining, we propose, in this paper, a trace exploration approach based on examining the trace execution phases. The approach consists of automatically identifying relevant information about the phases as well as the ability to provide an efficient representation of the flow of phases by detecting redundant phases using a cosine similarity metric. We applied our approach to large traces generated from two different systems and were able to quickly understand their content and extract higher level views that characterize the essence of the information conveyed in these traces.

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