A Similarity Integration Method based Information Retrieval and Word Embedding in Bug Localization

被引：12

作者：

Cheng, Shasha ^{[1
]}

Yan, Xuefeng ^{[1
]}

Khan, Arif Ali ^{[1
]}

机构：

[1] Nanjing Univ Aeronaut & Astronaut, Coll Comp Sci Technol, Collaborat Innovat Ctr Novel Software Technol & I, Nanjing, Peoples R China

来源：

2020 IEEE 20TH INTERNATIONAL CONFERENCE ON SOFTWARE QUALITY, RELIABILITY, AND SECURITY (QRS 2020) | 2020年

基金：

国家重点研发计划;

关键词：

software bug localization; information retrieval; word embedding; similarity integration; bug report;

D O I：

10.1109/QRS51102.2020.00034

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

To improve the performance of bug localization, there is necessity to solve the lexical mismatch between the natural language in the bug report and the programming language in the source file. A similarity integration method for bug localization is proposed, in which the similarity between bug report and source file is calculated by information retrieval (IR) and word embedding. More specifically, IR technique is used to collect the exact matches between bug report and source file. The terms in the bug report and the potential source files of different code tokens are connected by word embedding technique, which is used to complement with IR technique. Finally, deep neural network (DNN) is utilized to integrate extracted features to get the correlation between bug reports and source files. The experimental results show that the proposed approach outperforms several existing bug localization approaches in terms of Top N Rank, MAP, and MRR.

引用

页码：180 / 187

页数：8

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