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Combining Imbalance Learning Strategy and Multiclassifier Estimator for Bug Report Classification
المؤلفون المشاركون
Li, Hui
Guo, Shikai
Chen, Rong
Chen, Guo
Wang, Siwen
Wei, Miaomiao
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-16، 16ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-02-17
دولة النشر
مصر
عدد الصفحات
16
التخصصات الرئيسية
الملخص EN
Since a large number of bug reports are submitted to the bug repository every day, efficiently assigning bug reports to the correct developer is a considerable challenge.
Because of the large differences between the different components of different projects, the current bug classification mainly relies on the components of the bug report to dispatch bug reports to the designated developer or developer community.
Unfortunately, the component information of the bug report is filled in by default according to the bug submitter and the result is often incorrect.
Thus, an automatic technology that can identify high-impact bug reports can help developers to be aware of them early, rectify them quickly, and minimize the damages they cause.
In this paper, we propose a method based on the combination of imbalanced learning strategies such as random undersampling (RUS), random oversampling (ROS), synthetic minority oversampling technique (SMOTE), and AdaCost algorithms with multiclass classification methods, OVO and OVA, to solve bug reports component classification problem.
We investigate the effectiveness of different combinations, i.e., variants, each of which includes a specific imbalance learning strategy and a specific classification algorithm.
We mainly perform an analytical study on five open bug repositories (Eclipse, Mozilla, GCC, OpenOffice, and NetBeans).
The results show that different variants have different performance for bug reports component identification and the best performance variants are combined with the imbalanced learning strategy RUS and the OVA method based on the SVM classifier.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Guo, Shikai& Wang, Siwen& Wei, Miaomiao& Chen, Rong& Chen, Guo& Li, Hui. 2020. Combining Imbalance Learning Strategy and Multiclassifier Estimator for Bug Report Classification. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-16.
https://search.emarefa.net/detail/BIM-1196158
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Guo, Shikai…[et al.]. Combining Imbalance Learning Strategy and Multiclassifier Estimator for Bug Report Classification. Mathematical Problems in Engineering No. 2020 (2020), pp.1-16.
https://search.emarefa.net/detail/BIM-1196158
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Guo, Shikai& Wang, Siwen& Wei, Miaomiao& Chen, Rong& Chen, Guo& Li, Hui. Combining Imbalance Learning Strategy and Multiclassifier Estimator for Bug Report Classification. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-16.
https://search.emarefa.net/detail/BIM-1196158
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1196158
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر
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