Visualizing software dynamicities with heat maps

Benomar, Omar; Sahraoui, Houari; Poulin, Pierre

doi:10.1109/vissoft.2013.6650524

Cited by 17 publications

(10 citation statements)

References 27 publications

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“…ShiViz allows users to filter events and executions to reduce the visual load. Other techniques, such as aggregating the events or data [105], visualizing heatmaps [7,67], bundling edges [118], or animating the visualization [70] can also manage long executions. Inherently, these techniques aim to abstract away some details of the execution to make it possible to visualize a larger execution, while ShiViz aims to show as many details as is reasonable to help developers understand the relevant behavior.…”

Section: Visualizing Distributed Systemsmentioning

confidence: 99%

Visualizing Distributed System Executions

Beschastnikh

Liu

Xing

et al. 2020

ACM Trans. Softw. Eng. Methodol.

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Distributed systems pose unique challenges for software developers. Understanding the system’s communication topology and reasoning about concurrent activities of system hosts can be difficult. The standard approach, analyzing system logs, can be a tedious and complex process that involves reconstructing a system log from multiple hosts’ logs, reconciling timestamps among hosts with non-synchronized clocks, and understanding what took place during the execution encoded by the log. This article presents a novel approach for tackling three tasks frequently performed during analysis of distributed system executions: (1) understanding the relative ordering of events, (2) searching for specific patterns of interaction between hosts, and (3) identifying structural similarities and differences between pairs of executions. Our approach consists of XVector , which instruments distributed systems to capture partial ordering information that encodes the happens-before relation between events, and ShiViz , which processes the resulting logs and presents distributed system executions as interactive time-space diagrams. Two user studies with a total of 109 students and a case study with 2 developers showed that our method was effective, helping participants answer statistically significantly more system-comprehension questions correctly, with a very large effect size.

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Section: Visualizing Distributed Systemsmentioning

confidence: 99%

Visualizing Distributed System Executions

Beschastnikh

Liu

Xing

et al. 2020

ACM Trans. Softw. Eng. Methodol.

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“…Of particular interest was how Benomar [16] used heatmaps for easier understanding. Combining the radial design with the use of heat-map colours, we are able visualize the evolution of library dependencies and relative usage at that point in time.…”

Section: B Visualization Techniques and Representationsmentioning

confidence: 99%

“…The visualization are generated from 188,951 dependency facts. Details of the data can be downloaded from our website 16 .…”

Section: B Real World Examplesmentioning

confidence: 99%

Visualizing the Evolution of Systems and Their Library Dependencies

Kula

Roover

Germán

et al. 2014

2014 Second IEEE Working Conference on Software Visualization

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Abstract-System maintainers face several challenges stemming from a system and its library dependencies evolving separately. Novice maintainers may lack the historical knowledge required to efficiently manage an inherited system. While some libraries are regularly updated, some systems keep a dependency on older versions. On the other hand, maintainers may be unaware that other systems have settled on a different version of a library. In this paper, we visualize how the dependency relation between a system and its dependencies evolves from two perspectives. Our system-centric dependency plots (SDP) visualize the successive library versions a system depends on over time. The radial layout and heat-map metaphor provide visual clues about the change in dependencies along the system's release history. From this perspective, maintainers can navigate to a library-centric dependants diffusion plot (LDP). The LDP is a time-series visualization that shows the diffusion of users across the different versions of a library. We demonstrate on real-world systems how maintainers can benefit from our visualizations through four case scenarios.

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“…The Java Interactive Visualization Environment (JIVE) [22] is an online visualization and analysis system for Java supporting both forward and reverse execution, and graphical queries over program execution. Recent work [23] , combines the dynamics of execution and the dynamics of software evolution within a single framework, based on the VERSO visualization framework for constructing heat maps.…”

Section: Introductionmentioning

confidence: 99%