Towards Evidence-Based Understanding of Electronic Data Sources

Lianping Chen, Muhammad Ali Babar, He Zhang

    Publikation: Konference artikel i Proceeding eller bog/rapport kapitelKonferencebidrag i proceedingsForskningpeer review

    Abstract

    Identifying relevant papers from various Electronic Data Sources (EDS) is one of the key activities of conducting these kinds of studies. Hence, the selection of EDS for searching the potentially relevant papers is an important decision, which can affect a study’s coverage of relevant papers. Researchers usually select EDS mainly based on personal knowledge, experience, and preferences and/or recommendations by other researchers. We believe that building an evidence-based understanding of EDS can enable researchers to make more informed decisions about the selection of EDS. This paper reports our initial effort towards this end. We propose an initial set of metrics for characterizing the EDS from the perspective of the needs of secondary studies. We explain the usage and benefits of the proposed metrics using the data gathered from two secondary studies. We also tried to synthesize the data from the two studies and that from literature to provide initial evidence-based heuristics for EDS selection.
    OriginalsprogEngelsk
    Titel14th International Conference on Evaluation and Assessment in Software Engineering (EASE)
    ForlagBritish Computer Society
    Publikationsdato2010
    StatusUdgivet - 2010
    Begivenhed14th International Conference on Evaluation and Assessment in Software Engineering (EASE) - Keele, Storbritannien
    Varighed: 12 apr. 201013 apr. 2010
    Konferencens nummer: 14th
    http://www.scm.keele.ac.uk/ease/ease2010.html

    Konference

    Konference14th International Conference on Evaluation and Assessment in Software Engineering (EASE)
    Nummer14th
    Land/OmrådeStorbritannien
    ByKeele
    Periode12/04/201013/04/2010
    Internetadresse

    Emneord

    • Electronic Data Sources
    • Secondary Studies
    • Evidence-Based Selection
    • Heuristics
    • Knowledge-Based Metrics

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