Predicting Bug Severity using Customized Weighted Majority Voting Algorithms

被引：0
|
作者：
Awad, Michael A. [1 ]
ElNainay, Mustafa Y. [1 ]
Abougabal, Mohamed S. [1 ]
机构：
[1] Fac Engn, Dept Comp & Syst Engn, Alexandria, Egypt
来源：
2017 PROCEEDINGS OF THE JAPAN-AFRICA CONFERENCE ON ELECTRONICS, COMMUNICATIONS, AND COMPUTERS (JAC-ECC) | 2017年
关键词：
Machine Learning; Severity Prediction; Software Engineering; Supervised Classifications;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
One of the crucial attributes of bug report is severity. Accurate prediction of bug severity a be a huge contribution towards optimized software maintenance. In this paper, a new model that combines classification techniques based on Customized Cascading Weighted Majority Voting has been proposed. The proposed technique has been evaluated using datasets from open-source projects. The results show that the proposed technique has superior performance compared to other classification techniques.
引用
收藏
页码：170 / 175
页数：6
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