Ihre E-Mail wurde erfolgreich gesendet. Bitte prüfen Sie Ihren Maileingang.

Leider ist ein Fehler beim E-Mail-Versand aufgetreten. Bitte versuchen Sie es erneut.

Vorgang fortführen?

Exportieren
  • 1
    Online-Ressource
    Online-Ressource
    MDPI AG ; 2019
    In:  Applied Sciences Vol. 9, No. 16 ( 2019-08-13), p. 3327-
    In: Applied Sciences, MDPI AG, Vol. 9, No. 16 ( 2019-08-13), p. 3327-
    Kurzfassung: In a software tracking system, the bug assignment problem refers to the activities that developers perform during software maintenance to fix bugs. As many bugs are submitted on a daily basis, the number of developers required is quite large, and it therefore becomes difficult to assign the right developers to resolve issues with specific bugs. Inappropriate dispatches results in delayed processing of bug reports. In this paper, we propose an algorithm called ABC-DR to solve the bug assignment problem. The ABC-DR algorithm is a two-part composite approach that includes analysis between bug reports (i.e., B-based analysis) and analysis between developers and bug reports (i.e., D-based analysis). For analysis between bug reports, we use the multi-label k-nearest neighbor (ML-KNN) algorithm to find similar bug reports when compared with the new bug reports, and the developers who analyze similar bug reports recommend developers for the new bug report. For analysis between developers and bug reports, we find developer rankings similar to the new bug report by calculating the relevance scores between developers and similar bug reports. We use the artificial bee colony (ABC) algorithm to calculate the weight of each part. We evaluated the proposed algorithms on three datasets—GCC, Mozilla, and NetBeans—comparing ABC-DR with DevRec, DREX, and Bugzie. The experimental results show that the proposed ABC-DR algorithm achieves the highest improvement of 51.2% and 53.56% over DevRec for recall@5 and recall@10 in the NetBeans dataset.
    Materialart: Online-Ressource
    ISSN: 2076-3417
    Sprache: Englisch
    Verlag: MDPI AG
    Publikationsdatum: 2019
    ZDB Id: 2704225-X
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
    BibTip Andere fanden auch interessant ...
Schließen ⊗
Diese Webseite nutzt Cookies und das Analyse-Tool Matomo. Weitere Informationen finden Sie auf den KOBV Seiten zum Datenschutz