Multi-label classification and fuzzy similarity-based expert identification techniques for software bug assignment

被引：0
|
作者：
Panda, Rama Ranjan [1 ]
Nagwani, Naresh Kumar [2 ]
机构：
[1] Siksha O Anusandhan Deemed Univ, Dept Comp Sci & Engn, ITER, Bhubaneswar, Odisha, India
[2] Natl Inst Technol Raipur, Dept Comp Sci & Engn, Raipur, Chhattisgarh, India
来源：
INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE AND ENGINEERING | 2024年 / 27卷 / 06期
关键词：
bug assignment; expert finding; decision making; fuzzy logic; mining bug repositories; machine learning; fuzzy similarity;
D O I：
10.1504/IJCSE.2024.142837
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
In software development, a bug can occur due to multiple failures in software, and it may require multiple developers to fix it. In machine learning approaches, the bugs are assigned to a developer with a clear-cut outcome based on the agreed level of opinion from the assigner. However, instances of software bugs are textual and fuzzy. In this paper, two fuzzy systems: the fuzzy bug assignment technique for software developers and unique term relationships (FDUR) and the fuzzy bug assignment technique for software developers and category relationships (FDCR) are developed to measure the degree of relationships between developers, bugs, and its categories. The computed degree of relationship is used for handling the bugs with multiple categories and a set of developers involved in the development of software. To measure and compare the performance of both techniques with other existing techniques, the experiments are carried out on the benchmark software repositories.
引用
收藏
页码：734 / 748
页数：16
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