Towards an Open Source Stack to Create a Unified Data Source for Software Analysis and Visualization

Müller, Richard; Mahler, Dirk; Hunger, Michael; Nerche, Jens; Harrer, Markus

doi:10.1109/vissoft.2018.00019

Cited by 4 publications

(2 citation statements)

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“…Data structure of stack visual analysis uses stack, indexed array and pointer. The first operation is push to add new value in the stack and second operation pop uses to retrieve the value for interactive visualization [117].…”

Section: E Stack Visual Analysismentioning

confidence: 99%

A Comprehensive State-of-the-Art Survey on Data Visualization Tools: Research Developments, Challenges and Future Domain Specific Visualization Framework

et al. 2022

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Data visualization is a powerful skill for the demonstration of meaningful data insights in an interactive and effective way. In this survey article, we collected 70 articles from last five years (2017)(2018)(2019)(2020)(2021)(2022) to identify, classify, and investigate the various scopes, aspects and theories of data visualization. We also investigated the powerful applications of data visualization in various domains and fields such as visualization apps for health sector, Internet of things (IoTs), business dashboards, urban traffic management, smart buildings and environmental data visualization. However, after thorough investigation and classification, we conclude that, a comprehensive study is still missing about interactive, effective and efficient data visualization survey explaining basic current state-of-the-art best interactive visualization techniques, webbased tools and platforms, best performance theories, data structures and algorithms. In this survey article, we perform a thorough investigation to fill the gap on theoretical, analytical, statistical models and techniques for improving the performance of visualization. Current primary and domain specific future challenges are also reviewed, and related future research directions and opportunities are recommended. 13 14 ''Interactive'' and ''Web-based'' are the most searched key-42 words during journals analysis process.43 A. TAXONOMY OF DATA VISUALIZATION ARTICLES 44 The taxonomy of data visualization is laborious task. Since, 45 several data visualization techniques, tools and platforms are 46 available to generate effective and interactive visualization. 47 However, in this survey, we classify data visualization with 48 respect to various scopes and applications where advanced 49 and interactive visualization techniques are still required to 50 make better informed decisions. In the first stage, this sur-51 vey collects 70 articles from last five years (2017-2022) 52 to identify the various scopes, aspects and application of 53 data visualization. We divide the 65 articles into 7 different 54 scopes. We also classify these articles according to their 55 domain research and approaches. The contribution of each 56 article has been highlighted and cited in the Table 1. In this 57 initial stage, this survey discusses the various aspects of 58 7 scopes. In Scope-1, the techniques to handling of various 59 types of data, graphs, colours interaction and its integration 60 are discussed. In Scope-2, the focus is on the data mining 61 networks, environment and structure. In Scope-3, the role of 62 decision-making techniques in data visualization is empha-63 sised. In Scope-4, data visualization for big data and its 64 emerging applications are analysed. Scope-5 shares the visu-65 alization competencies in data security. In Scope-6, the key 66 role of statistical analysis for visualization is explored. In the 67 final Scope-7, various applications are reviewed to check 68 the performance of visualization in specific domain. The 69 contribution of each sco...

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Section: E Stack Visual Analysismentioning

confidence: 99%

A Comprehensive State-of-the-Art Survey on Data Visualization Tools: Research Developments, Challenges and Future Domain Specific Visualization Framework

et al. 2022

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“…We modi ed TypeChef to serialize the complete AST to an XML le for further processing with jQAssistant. is is a program for analyzing and visualisizing so ware artifacts [18]. It is built on top of Neo4j, a graph database.…”

Section: Variablity Extractionmentioning

confidence: 99%

Understanding Conditional Compilation through Integrated Representation of Variability and Source Code

Baum

Sixtus

Vogelsberg

et al. 2019

Proceedings of the 23rd International Systems and Software Product Line Conference - Volume B

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e C preprocessor (CPP) is a standard tool for introducing variability into source programs and is o en applied either implicitly or explicitly for implementing a So ware Product Line (SPL). Despite its practical relevance, CPP has many drawbacks. Because of that it is very di cult to understand the variability implemented using CPP. To facilitate this task we provide an innovative analytics tool which bridges the gap between feature models as more abstract representations of variability and its concrete implementation with the means of CPP. It allows to interactively explore the entities of a source program with respect to the variability realized by conditional compilation. us, it simpli es tracing and understanding the e ect of enabling or disabling feature ags.

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Visualization of Software Architectures in Virtual Reality and Augmented Reality

Schreiber

Nafeie

Baranowski

et al. 2019

2019 IEEE Aerospace Conference

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Towards an Open Source Stack to Create a Unified Data Source for Software Analysis and Visualization

Cited by 4 publications

References 10 publications

A Comprehensive State-of-the-Art Survey on Data Visualization Tools: Research Developments, Challenges and Future Domain Specific Visualization Framework

A Comprehensive State-of-the-Art Survey on Data Visualization Tools: Research Developments, Challenges and Future Domain Specific Visualization Framework

Understanding Conditional Compilation through Integrated Representation of Variability and Source Code

Visualization of Software Architectures in Virtual Reality and Augmented Reality

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