Variability Debt: A Multi-method Study

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dc.contributor.authorWolfart, Daniele
dc.contributor.authorAssunção, Wesley K.Guez
dc.contributor.authorMartinez, Jabier
dc.contributor.institutionSWT
dc.date.accessioned2024-07-24T11:52:01Z
dc.date.available2024-07-24T11:52:01Z
dc.date.issued2023-11-07
dc.descriptionPublisher Copyright: © 2023 ACM.
dc.description.abstractTechnical debt is a metaphor to guide the identification, measurement, and general management of decisions that were mostly appropriate in the short term but created obstacles mainly for the evolution and maintenance of systems. Variability management, which is the ability to create variants of systems to satisfy different needs, is a potential source of technical debt. Variability debt, a term coined in this work, is caused by sub-optimal solutions in the implementation of variability management in software systems. We performed a systematic literature review to characterize variability debt, and conducted a field study in which we report quantitative and qualitative analysis based on documents (e.g., requirements, specifications, source code, and test cases) and a survey with stakeholders. The context is a large company with three different systems, where opportunistic reuse was applied to create variants for each system. We describe and characterize the variability debt phenomenon in this field study, and we assess the validity of the metaphor to create awareness in diverse company stakeholders and to guide technical debt management research related to variability aspects. The analysis of the field study's artifacts show evidences of factors that complicate the evolution of the variants, such as code duplication and non-synchronized artifacts. Time pressure is identified as the main cause for not considering other options than opportunistic reuse. Technical practitioners mostly agree on the creation of usability problems and complex maintenance of multiple independent variants. However, this is not fully perceived by managerial practitioners.en
dc.description.statusPeer reviewed
dc.format.extent10
dc.identifier.citationWolfart , D , Assunção , W K G & Martinez , J 2023 , Variability Debt : A Multi-method Study . in SBQS 2023 - Proceedings of the 22nd Brazilian Symposium on Software Quality . ACM International Conference Proceeding Series , Association for Computing Machinery , pp. 358-367 , 22nd Brazilian Symposium on Software Quality, SBQS 2023 , Brasilia , Brazil , 7/11/23 . https://doi.org/10.1145/3629479.3629513
dc.identifier.citationconference
dc.identifier.doi10.1145/3629479.3629513
dc.identifier.isbn9798400707865
dc.identifier.urihttps://hdl.handle.net/11556/2145
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85180148148&partnerID=8YFLogxK
dc.language.isoeng
dc.publisherAssociation for Computing Machinery
dc.relation.ispartofSBQS 2023 - Proceedings of the 22nd Brazilian Symposium on Software Quality
dc.relation.ispartofseriesACM International Conference Proceeding Series
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.subject.keywordsSoftware reuse
dc.subject.keywordsTechnical Debt
dc.subject.keywordsVariability Debt
dc.subject.keywordsVariability management
dc.subject.keywordsHuman-Computer Interaction
dc.subject.keywordsComputer Networks and Communications
dc.subject.keywordsComputer Vision and Pattern Recognition
dc.subject.keywordsSoftware
dc.titleVariability Debt: A Multi-method Studyen
dc.typeconference output
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