STRESS : a semi-automated, fully replicable approach for project selection

D. Falessi, W. Smith, A. Serebrenik

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

    8 Citations (Scopus)

    Abstract

    The mining of software repositories has provided significant advances in a multitude of software engineering fields, including defect prediction. Several studies show that the performance of a software engineering technology (e.g., prediction model) differs across different project repositories. Thus, it is important that the project selection is replicable. The aim of this paper is to present STRESS, a semi-automated and fully replicable approach that allows researchers to select projects by configuring the desired level of diversity, fit, and quality. STRESS records the rationale behind the researcher decisions and allows different users to re-run or modify such decisions. STRESS is open-source and it can be used used locally or even online (www.falessi.com/STRESS/). We perform a systematic mapping study that considers studies that analyzed projects managed with JIRA and Git to asses the project selection replicability of past studies. We validate the feasible application of STRESS in realistic research scenarios by applying STRESS to select projects among the 211 Apache Software Foundation projects. Our systematic mapping study results show that none of the 68 analyzed studies is completely replicable. Regarding STRESS, it successfully supported the project selection among all 211 ASF projects. It also supported the measurement of 100 projects characteristics, including the 32 criteria of the studies analyzed in our mapping study. The mapping study and STRESS are, to our best knowledge, the first attempt to investigate and support the replicability of project selection. We plan to extend them to other technologies such as GitHub.
    Original languageEnglish
    Title of host publicationProceedings - 11th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, ESEM 2017
    Place of PublicationPiscataway
    PublisherInstitute of Electrical and Electronics Engineers
    Pages151-156
    Number of pages6
    ISBN (Electronic)978-1-5090-4039-1
    ISBN (Print)978-1-5090-4040-7
    DOIs
    Publication statusPublished - 7 Dec 2017
    EventACM/IEEE International Symposium on Empirical Software Engineering and Measurement, 9-10 November 2017, Toronto, Canada - Toronto, Canada
    Duration: 9 Nov 201710 Nov 2017
    http://www.scs.ryerson.ca/eseiw2017/ESEM/

    Conference

    ConferenceACM/IEEE International Symposium on Empirical Software Engineering and Measurement, 9-10 November 2017, Toronto, Canada
    Abbreviated titleESEM2017
    CountryCanada
    CityToronto
    Period9/11/1710/11/17
    Internet address

    Keywords

    • Apache
    • mining software repositories
    • replication

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