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Quality Models Inside Out: Interactive Visualization of Software Metrics by Means of Joint Probabilities
Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM).
Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM).
Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM). (ISOVIS)ORCID-id: 0000-0002-2901-935X
Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM). (DISA ; DSIQ)ORCID-id: 0000-0003-1173-5187
Vise andre og tillknytning
2018 (engelsk)Inngår i: 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, s. 65-75Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
IEEE, 2018. s. 65-75
Emneord [en]
hierarchical data exploration, multivariate data visualization, joint probabilities, t-SNE, data abstraction
HSV kategori
Forskningsprogram
Datavetenskap, Informations- och programvisualisering; Datavetenskap, Programvaruteknik
Identifikatorer
URN: urn:nbn:se:lnu:diva-78093DOI: 10.1109/VISSOFT.2018.00015Scopus ID: 2-s2.0-85058463111ISBN: 978-1-5386-8292-0 (digital)ISBN: 978-1-5386-8293-7 (tryckt)OAI: oai:DiVA.org:lnu-78093DiVA, id: diva2:1252281
Konferanse
IEEE Working Conference on Software Visualization (VISSOFT), Madrid, Spain, 24-25 September, 2018
Prosjekter
Software technology for self-adaptive systems
Forskningsfinansiär
Knowledge Foundation, 20150088Tilgjengelig fra: 2018-10-01 Laget: 2018-10-01 Sist oppdatert: 2019-08-29bibliografisk kontrollert

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