An Improved Discriminative Model for Duplication Detection on Bug Reports with Cluster Weighting

2014 IEEE 38th Annual Computer Software and Applications Conference Pub Date : 2014-07-21 DOI:10.1109/COMPSAC.2014.18

Meng-Jie Lin, Cheng-Zen Yang

引用次数: 6

Abstract

Processing bug reports plays an important role for software maintenance. Recently, the issue of detecting duplicate bug reports has been noticed due to their considerable appearances. In the past, many NLP-based detection schemes have been proposed. However, the cluster-level correlation relationships are not extensively considered in the past studies. In this paper, we present an improved detection scheme using cluster weighting to enhance the detection performance of a previous SVM-based method. We have conducted empirical studies with three open source software projects, Apache, ArgoUML, and SVN. Compared with the original SVM-based method, the proposed SVM-TC scheme can achieve 2.83-16.32% improvements of the top-5 recall rates in three projects.

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基于聚类加权的Bug报告重复检测改进判别模型

处理bug报告在软件维护中起着重要的作用。最近，由于重复错误报告的大量出现，人们注意到了检测重复错误报告的问题。在过去，已经提出了许多基于nlp的检测方案。然而，在过去的研究中，聚类水平的相关关系并没有得到广泛的考虑。在本文中，我们提出了一种改进的检测方案，使用聚类加权来提高先前基于支持向量机的方法的检测性能。我们对Apache、ArgoUML和SVN这三个开源软件项目进行了实证研究。与原基于svm的方法相比，本文提出的SVM-TC方案在三个项目中召回率前5名的提高幅度为2.83 ~ 16.32%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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