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

Author

Meng-Jie Lin ; Cheng-Zen Yang

Author_Institution

Dept. of Comput. Sci. & Eng., Yuan Ze Univ., Chungli, Taiwan

fYear

2014

fDate

21-25 July 2014

Firstpage

117

Lastpage

122

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.

Keywords

natural language processing; pattern clustering; program debugging; public domain software; software maintenance; support vector machines; Apache; ArgoUML; NLP-based detection scheme; SVM-TC scheme; SVM-based method; SVN; cluster weighting; cluster-level correlation relationship; detection performance; discriminative model; duplicate bug reports; duplication detection; open source software project; software maintenance; Correlation; Feature extraction; Mathematical model; Software; Support vector machines; Training; Vectors; Bug Reports; Cluster Weighting; Duplication Detection; Empirical Study;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Software and Applications Conference (COMPSAC), 2014 IEEE 38th Annual

Conference_Location

Vasteras

Type

conf

DOI

10.1109/COMPSAC.2014.18

Filename

6899208