کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
396684 670547 2015 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Software defect prediction using a cost sensitive decision forest and voting, and a potential solution to the class imbalance problem
ترجمه فارسی عنوان
پیش بینی نقص نرم افزار با استفاده از یک تصمیم حساس تصمیم گیری و رای گیری و یک راه حل بالقوه برای مشکل عدم تعادل کلاس
کلمات کلیدی
پیش بینی نقص نرم افزار، تصمیم گیری جنگل، حساس به هزینه رأی دادن جنگل، عدم تعادل کلاس
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی

Author-Highlights
• SDP is short for Software Defect Prediction.
• We show that there is not a clear winner in the studied existing methods for SDP⁎.
• A cost-sensitive decision forest and voting technique are proposed.
• The superiority of the proposed techniques is shown.
• A proposed framework for the forest algorithm for handling class imbalance.

Software development projects inevitably accumulate defects throughout the development process. Due to the high cost that defects can incur, careful consideration is crucial when predicting which sections of code are likely to contain defects. Classification algorithms used in machine learning can be used to create classifiers which can be used to predict defects. While traditional classification algorithms optimize for accuracy, cost-sensitive classification methods attempt to make predictions which incur the lowest classification cost. In this paper we propose a cost-sensitive classification technique called CSForest which is an ensemble of decision trees. We also propose a cost-sensitive voting technique called CSVoting in order to take advantage of the set of decision trees in minimizing the classification cost. We then investigate a potential solution to class imbalance within our decision forest algorithm. We empirically evaluate the proposed techniques comparing them with six (6) classifier algorithms on six (6) publicly available clean datasets that are commonly used in the research on software defect prediction. Our initial experimental results indicate a clear superiority of the proposed techniques over the existing ones.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Information Systems - Volume 51, July 2015, Pages 62–71
نویسندگان
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