Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2015
License: CC BY
Data sources: ZENODO
addClaim

This Research product is the result of merged Research products in OpenAIRE.

You have already added 0 works in your ORCID record related to the merged Research product.

Experimental Data for: Hierarchical Software Landscape Visualization for System Comprehension: A Controlled Experiment

Authors: Fittkau, Florian; Krause, Alexander; Hasselbring, Wilhelm;

Experimental Data for: Hierarchical Software Landscape Visualization for System Comprehension: A Controlled Experiment

Abstract

In many enterprises the number of deployed applications is constantly increasing. Those applications - often several hundreds - form large software landscapes. The comprehension of such landscapes is frequently impeded due to, for instance, architectural erosion, personnel turnover, or changing requirements. Therefore, an efficient and effective way to comprehend such software landscapes is required. The current state of the art often visualizes software landscapes via flat graph-based representations of nodes, applications, and their communication. In our ExplorViz visualization, we introduce hierarchical abstractions aiming at solving typical system comprehension tasks fast and accurately for large software landscapes. To evaluate our hierarchical approach, we conduct a controlled experiment comparing our hierarchical landscape visualization to a flat, state-of-the-art visualization. In addition, we thoroughly analyze the strategies employed by the participants and provide a package containing all our experimental data to facilitate the verifiability, reproducibility, and further extensibility of our results. We observed a statistically significant increase of 14 % in task correctness of the hierarchical visualization group compared to the flat visualization group in our experiment. The time spent on the system comprehension tasks did not show any significant differences. The results backup our claim that our hierarchical concept enhances the current state of the art in landscape visualization. This package contains our experimental data.

Keywords

Software Visualization

Powered by OpenAIRE graph
Found an issue? Give us feedback