Visualization of Source Code Similarity Using 2.5D Semantic Software Maps

Atzberger, Daniel; Cech, Tim; Scheibel, Willy; Limberger, Daniel; Döllner, Jürgen

doi:10.1007/978-3-031-25477-2_8

Cited by 3 publications

(2 citation statements)

References 51 publications

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“…Especially, 2.5D treemaps provide further visual attributes, which motivates their use for exploring large software projects by means of code cities [48], software cities [49], or software maps [5]. Besides hierarchy-preserving visualizations, layouts can also be generated based on the semantic composition of software projects [50,51]. In this case, abstract concepts in the source code are captured by applying a topic model, which results in a high-dimensional representation of each source code file.…”

Section: Software Visualizationmentioning

confidence: 99%

Integrated Visual Software Analytics on the GitHub Platform

Scheibel,

Blum,

Lauterbach

et al. 2024

Computers

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Readily available software analysis and analytics tools are often operated within external services, where the measured software analysis data are kept internally and no external access to the data is available. We propose an approach to integrate visual software analysis on the GitHub platform by leveraging GitHub Actions and the GitHub API, covering both analysis and visualization. The process is to perform software analysis for each commit, e.g., static source code complexity metrics, and augment the commit using the resulting data, stored as git objects within the same repository. We show that this approach is feasible by integrating it into 64 open source TypeScript projects. Furthermore, we analyze the impact on Continuous Integration (CI) run time and repository storage. The stored software analysis data are externally accessible to allow for visualization tools, such as software maps. The effort to integrate our approach is limited to enabling the analysis component within a project’s CI on GitHub and embed an HTML snippet into the project’s website for visualization. This enables a large amount of projects to have access to software analysis as well as provide means to communicate the current status of a project.

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Section: Software Visualizationmentioning

confidence: 99%

Integrated Visual Software Analytics on the GitHub Platform

Scheibel,

Blum,

Lauterbach

et al. 2024

Computers

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“…The innovations in environmental and geological analysis have led to new research directions in software engineering. Atzberger et al [23] made a significant contribution with their method of visualizing source code similarity through the use of 2.5D semantic software maps. Their method involved placing 3D glyphs on a two-dimensional plane to represent semantic relationships within source code, offering a novel method that facilitates enhanced code review and bug detection processes.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Visual Representation for Java Code Data in the Field of Software Engineering Based on Remote Sensing Techniques

Li,

Wang,

Zhao

2023

Electronics

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In the field of software engineering, large and complex code bases may lead to some burden of understanding their structure and meaning for developers. To reduce the burden on developers, we consider a code base visualization method to visually express the meaning of code bases. Inspired by remote sensing imagery, we employ graphical representations to illustrate the semantic connections within Java code bases, aiming to help developers understand its meaning and logic. This approach is segmented into three distinct levels of analysis. First, at the project-level, we visualize Java projects by portraying each file as an element within a code forest, offering a broad overview of the project’s structure. This macro-view perspective aids in swiftly grasping the project’s layout and hierarchy. Second, at the file-level, we concentrate on individual files, using visualization techniques to highlight their unique attributes and complexities. This perspective enables a deeper understanding of each file’s structure and its role within the larger project. Finally, at the component-level, our focus shifts to the detailed analysis of Java methods and classes. We examine these components for complexity and other specific characteristics, providing insights that are crucial for the optimization of code and the enhancement of software quality. By integrating remote sensing technology, our method offers software engineers deeper insights into code quality, significantly enhancing the software development lifecycle and its outcomes.

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