Research Progress of Software Defect Prediction

被引：0
|
作者：
Gong L.-N. [1 ,2 ,3 ]
Jiang S.-J. [1 ,2 ]
Jiang L. [1 ,2 ]
机构：
[1] School of Computer Science and Technology, China University of Mining and Technology, Xuzhou
[2] Engineering Research Center of Mine Digitalization of Ministry of Education, Xuzhou
[3] College of Information Science and Engineering, Zaozhuang University, Zaozhuang
来源：
Ruan Jian Xue Bao/Journal of Software | 2019年 / 30卷 / 10期
基金：
中国国家自然科学基金;
关键词：
Data preprocessing; Machine learning; Performance evaluation criteria; Software defect prediction (SDP); Software metrics;
D O I：
10.13328/j.cnki.jos.005790
中图分类号：
学科分类号：
摘要：
With the improvement of the scale and complexity of software, software quality problems become the focus of attention. Software defect is the opposite of software quality, threatening the software quality. How to dig up defect modules in the early stages of software development has become a urgent problem that needs to be solved. Software defect prediction (SDP) designs the internal metrics related defects by mining software history repositories, and then in advance finds and locks the defect modules with the aid of machine learning methods, so as to allocate the limited resources reasonably. Therefore, SDP is one of the important ways of software quality assurance (SQA), which has become a very important research subject in software engineering in recent years. Based on the form of defect perfection, this research offers a systematic analysis of the existing research achievements of the domestic and foreign researchers in recent eight years (2010~2017). First, the research framework of SDP is given.Then the existing research achievements are classified and compared from three aspects, including datasets of SDP, the methods models and the evaluation indicators. Finally, the possible research directions are pointed out. © Copyright 2019, Institute of Software, the Chinese Academy of Sciences. All rights reserved.
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页码：3090 / 3114
页数：24
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