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Introducing Quality Models Based On Joint Probabilities: Introducing Quality Models Based On 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). (DISA ; DSIQ)ORCID iD: 0000-0002-7565-3714
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). (DISA ; DSIQ)ORCID iD: 0000-0003-1173-5187
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).ORCID iD: 0000-0002-0835-823X
2018 (English)In: ICSE '18 Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings, IEEE, 2018, p. 216-217Conference paper, Poster (with or without abstract) (Refereed)
Abstract [en]

Multi-dimensional goals can be formalized in so-called quality models. Often, each dimension is assessed with a set of metrics that are not comparable; they come with different units, scale types, and distributions of values. Aggregating the metrics to a single quality score in an ad-hoc manner cannot be expected to provide a reliable basis for decision making. Therefore, aggregation needs to be mathematically well-defined and interpretable. We present such a way of defining quality models based on joint probabilities. We exemplify our approach using a quality model with 30 standard metrics assessing technical documentation quality and study ca. 20,000 real-world files. We study the effect of several tests on the independence and results show that metrics are, in general, not independent. Finally, we exemplify our suggested definition of quality models in this domain.

Place, publisher, year, edition, pages
IEEE, 2018. p. 216-217
Series
Proceedings of the IEEE-ACM International Conference on Software Engineering Companion, ISSN 2574-1926
Keywords [en]
Quality assessment, Software metrics, Bayesian networks
National Category
Software Engineering
Research subject
Computer Science, Software Technology
Identifiers
URN: urn:nbn:se:lnu:diva-79015DOI: 10.1145/3183440.3195103ISI: 000450109000078Scopus ID: 2-s2.0-85049686901ISBN: 978-1-4503-5663-3 (print)OAI: oai:DiVA.org:lnu-79015DiVA, id: diva2:1268524
Conference
40th ACM/IEEE International Conference on Software Engineering (ICSE), MAY 27-JUN 03, 2018, Gothenburg, SWEDEN
Available from: 2018-12-06 Created: 2018-12-06 Last updated: 2019-08-29Bibliographically approved

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Ulan, MariaLöwe, WelfEricsson, MorganWingkvist, Anna

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