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

Abstract

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.

Identifying computational phases from inter-process communication traces of HPC applications

“…Pirzadeh and Hamou-Lhadj presented a novel phase detection approach that they called trace segmentation and which was inspired by the way the human perception system groups lines and dots into shapes and objects [23,30]. They have developed several methods that could automatically group trace events into dense elements that formed computational phases.…”

“…For example, Process 7 will send to and receive from processes 2, 3,4,6,8,10,11,12,18,19,20,21,22,23,24,26,27, and 28.…”

Enhancing the Analysis of Large Multimedia Applications Execution Traces with FrameMiner

“…Pirzadeh et al in [2] use analysis of execution traces to understand behavioural aspects of complex software systems. They can divide the content of a trace into meaningful trace segments called execution phases, and their slicing is done using Gestalt laws.…”

“…However, the programmer has to go through a series of pages in order to find a specific information. Our work divides an input trace into relevant blocks, mutually independents, without any information on some execution part, unlike [2,10]. The visualization step becomes now easier to manage through information grouping, and the analysis is improved.…”

“…These techniques can obtain a reduced execution trace but not always representative of the entire trace [9]. Pirzadeh et al introduced in [2] that the general consensus in the trace analysis community is to emphasise the work towards effective trace abstraction techniques, such as [10]. For reducing the volume of events in a trace to be analysed by the programmer, we propose to abstract series of lowlevel events as blocks that are meaningful to the programmer, and then to further abstract the trace as sequences of blocks.…”