Using similarity metrics for mining variability from software repositories

Mike Mannion, Hermann Kaindl

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    35 Downloads (Pure)

    Abstract

    Much activity within software product line engineering has been
    concerned with explicitly representing and exploiting
    commonality and variability at the feature level for the purpose of
    a particular engineering task e.g. requirements specification,
    design, coding, verification, product derivation process, but not
    for comparing how similar products in the product line are with
    each other. In contrast, a case-based approach to software
    development is concerned with descriptions and models as a set of
    software cases stored in a repository for the purpose of searching
    at a product level, typically as a foundation for new product
    development. New products are derived by finding the most
    similar product descriptions in the repository using similarity
    metrics.
    The new idea is to use such similarity metrics for mining
    variability from software repositories. In this sense, software
    product line engineering could be informed by the case-based
    approach. This approach requires defining and implementing
    such similarity metrics based on the representations used for the
    software cases in such a repository. It provides complementary
    benefits to the ones given through feature-based representations of
    variability and may help mining such variability.
    Original languageEnglish
    Title of host publicationProceedings of the 18th International Software Product Line Conference
    Place of PublicationNew York
    PublisherACM
    Pages32-35
    Number of pages3
    Volume2
    ISBN (Electronic)978-1-4503-2739-8
    DOIs
    Publication statusPublished - 15 Sep 2014

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    Specifications

    Keywords

    • software engineering
    • product lines
    • commonality and variability
    • feature-based representation
    • case-based reasoning
    • similarity metrics

    Cite this

    Mannion, M., & Kaindl, H. (2014). Using similarity metrics for mining variability from software repositories. In Proceedings of the 18th International Software Product Line Conference (Vol. 2, pp. 32-35). New York: ACM. https://doi.org/10.1145/2647908.2655964
    Mannion, Mike ; Kaindl, Hermann. / Using similarity metrics for mining variability from software repositories. Proceedings of the 18th International Software Product Line Conference. Vol. 2 New York : ACM, 2014. pp. 32-35
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    Mannion, M & Kaindl, H 2014, Using similarity metrics for mining variability from software repositories. in Proceedings of the 18th International Software Product Line Conference. vol. 2, ACM, New York, pp. 32-35. https://doi.org/10.1145/2647908.2655964

    Using similarity metrics for mining variability from software repositories. / Mannion, Mike; Kaindl, Hermann.

    Proceedings of the 18th International Software Product Line Conference. Vol. 2 New York : ACM, 2014. p. 32-35.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

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    Mannion M, Kaindl H. Using similarity metrics for mining variability from software repositories. In Proceedings of the 18th International Software Product Line Conference. Vol. 2. New York: ACM. 2014. p. 32-35 https://doi.org/10.1145/2647908.2655964