A Quantitative Analysis of Variability Warnings in Linux

Jean Melo, Elvis Flesborg, Claus Brabrand, Andrzej Wasowski

    Research output: Conference Article in Proceeding or Book/Report chapterArticle in proceedingsResearchpeer-review

    Abstract

    In order to get insight into challenges with quality in highly-configurable software, we analyze one of the largest open source projects, the Linux kernel, and quantify basic properties of configuration-related warnings. We automatically analyze more than 20 thousand valid and distinct random configurations, in a computation that lasted more than a month. We count and classify a total of 400,000 warnings to get an insight in the distribution of warning types, and the location of the warnings. We run both on a stable and unstable version of the Linux kernel. The results show that Linux contains a significant amount of configuration-dependent warnings, including many that appear harmful. In fact, it appears that there are no configuration-independent warnings in the kernel at all, adding to our knowledge about relevance of family based analyses.
    Original languageEnglish
    Title of host publicationVaMoS '16 Proceedings of the Tenth International Workshop on Variability Modelling of Software-intensive Systems
    PublisherAssociation for Computing Machinery
    Publication date10 Dec 2015
    Pages3-8
    ISBN (Print)978-1-4503-4019-9
    DOIs
    Publication statusPublished - 10 Dec 2015
    EventInternational Workshop on Variability Modelling of Software-intensive Systems - Mercure Salvador Rio Vermelho Hotel, Salvador, Bahia, Brazil
    Duration: 27 Jan 201629 Jan 2016
    https://vamos2016.wordpress.com/

    Workshop

    WorkshopInternational Workshop on Variability Modelling of Software-intensive Systems
    LocationMercure Salvador Rio Vermelho Hotel
    Country/TerritoryBrazil
    CitySalvador, Bahia
    Period27/01/201629/01/2016
    Internet address

    Keywords

    • highly-configurable software
    • Linux kernel
    • configuration-related warnings
    • software quality analysis
    • random configurations

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