Deep Learning-based Production and Test Bug Report Classification using Source Files

被引：0
|
作者：
Kim, Misoo [1 ]
Kim, Youngkyoung [2 ]
Lee, Eunseok [3 ]
机构：
[1] Sungkyunkwan Univ, Inst Software Convergence, Suwon, South Korea
[2] Sungkyunkwan Univ, Dept Elect & Comp Engn, Suwon, South Korea
[3] Sungkyunkwan Univ, Coll Comp & Informat, Suwon, South Korea
来源：
2022 ACM/IEEE 44TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING: COMPANION PROCEEDINGS (ICSE-COMPANION 2022) | 2022年
基金：
新加坡国家研究基金会;
关键词：
Production bug; test bug; bug report classification; deep learning; information retrieval-based bug localization;
D O I：
10.1145/3510454.3528646
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
Classifying production and test bug reports can significantly improve not only the accuracy of performance evaluation but also the performance of information retrieval-based bug localization (IRBL). However, it is time-consuming for developers to classify these bug reports manually. This study proposes a production and test bug report classification method based on deep learning. Our method uses a set of source files and model tuning to solve the problem of insufficient and sparse bug reports when applying deep learning. Our experimental results reveal that the macro fl-score of our method is 0.84 and can improve the IRBL performance by 20%.
引用
收藏
页码：343 / 344
页数：2
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