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Quality Models Inside Out: Interactive Visualization of Software Metrics by Means of Joint Probabilities
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). (ISOVIS)ORCID iD: 0000-0002-2901-935X
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). (DISA ; DSIQ)ORCID iD: 0000-0003-1173-5187
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2018 (English)In: Proceedings of the 2018 Sixth IEEE Working Conference on Software Visualization, (VISSOFT), Madrid, Spain, 2018 / [ed] J. Ángel Velázquez Iturbide, Jaime Urquiza Fuentes, Andreas Kerren, and Mircea F. Lungu, IEEE, 2018, p. 65-75Conference paper, Published paper (Refereed)
Abstract [en]

Assessing software quality, in general, is hard; each metric has a different interpretation, scale, range of values, or measurement method. Combining these metrics automatically is especially difficult, because they measure different aspects of software quality, and creating a single global final quality score limits the evaluation of the specific quality aspects and trade-offs that exist when looking at different metrics. We present a way to visualize multiple aspects of software quality. In general, software quality can be decomposed hierarchically into characteristics, which can be assessed by various direct and indirect metrics. These characteristics are then combined and aggregated to assess the quality of the software system as a whole. We introduce an approach for quality assessment based on joint distributions of metrics values. Visualizations of these distributions allow users to explore and compare the quality metrics of software systems and their artifacts, and to detect patterns, correlations, and anomalies. Furthermore, it is possible to identify common properties and flaws, as our visualization approach provides rich interactions for visual queries to the quality models’ multivariate data. We evaluate our approach in two use cases based on: 30 real-world technical documentation projects with 20,000 XML documents, and an open source project written in Java with 1000 classes. Our results show that the proposed approach allows an analyst to detect possible causes of bad or good quality.

Place, publisher, year, edition, pages
IEEE, 2018. p. 65-75
Keywords [en]
hierarchical data exploration, multivariate data visualization, joint probabilities, t-SNE, data abstraction
National Category
Human Computer Interaction Software Engineering
Research subject
Computer Science, Information and software visualization; Computer Science, Software Technology
Identifiers
URN: urn:nbn:se:lnu:diva-78093DOI: 10.1109/VISSOFT.2018.00015Scopus ID: 2-s2.0-85058463111ISBN: 978-1-5386-8292-0 (electronic)ISBN: 978-1-5386-8293-7 (print)OAI: oai:DiVA.org:lnu-78093DiVA, id: diva2:1252281
Conference
IEEE Working Conference on Software Visualization (VISSOFT), Madrid, Spain, 24-25 September, 2018
Projects
Software technology for self-adaptive systems
Funder
Knowledge Foundation, 20150088Available from: 2018-10-01 Created: 2018-10-01 Last updated: 2019-08-29Bibliographically approved

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Ulan, MariaHönel, SebastianMartins, Rafael MessiasEricsson, MorganLöwe, WelfWingkvist, AnnaKerren, Andreas

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Ulan, MariaHönel, SebastianMartins, Rafael MessiasEricsson, MorganLöwe, WelfWingkvist, AnnaKerren, Andreas
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