A literature review and comparison of three feature location techniques using ArgoUML-SPL

Daniel Cruz, Eduardo Figueiredo, Jabier Martinez

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    22 Citations (Scopus)

    Abstract

    Over the last decades, the adoption of Software Product Line (SPL) engineering for supporting software reuse has increased. An SPL can be extracted from one single product or from a family of related software products, and feature location strategies are widely used for variability mining. Several feature location strategies have been proposed in the literature and they usually aim to map a feature to its source code implementation. In this paper, we present a systematic literature review that identifies and characterizes existing feature location strategies. We also evaluated three different strategies based on textual information retrieval in the context of the ArgoUML-SPL feature location case study. In this evaluation, we compare the strategies based on their ability to correctly identify the source code of several features from ArgoUML-SPL ground truth. We then discuss the strengths and weaknesses of each feature location strategy.

    Original languageEnglish
    Title of host publicationProceedings of the 13th International Workshop on Variability Modelling of Software-Intensive Systems, VAMOS 2019
    PublisherAssociation for Computing Machinery
    ISBN (Electronic)9781450366489
    DOIs
    Publication statusPublished - 6 Feb 2019
    Event13th International Workshop on Variability Modelling of Software-Intensive Systems, VAMOS 2019 - Leuven, Belgium
    Duration: 6 Feb 2019 → …

    Publication series

    NameACM International Conference Proceeding Series

    Conference

    Conference13th International Workshop on Variability Modelling of Software-Intensive Systems, VAMOS 2019
    Country/TerritoryBelgium
    CityLeuven
    Period6/02/19 → …

    Keywords

    • Benchmark
    • Feature location
    • Reverse engineering
    • Software product lines
    • Variability mining

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