Machine Learning-based Software Quality Prediction Models: State of the Art

被引：0

作者：

Al-Jamimi, Hamdi A. ^{[1
]}

Ahmed, Moataz ^{[1
]}

机构：

[1] King Fahd Univ Petr & Minerals, Informat & Comp Sci Dept, Dhahran 31261, Saudi Arabia

来源：

2013 INTERNATIONAL CONFERENCE ON INFORMATION SCIENCE AND APPLICATIONS (ICISA 2013) | 2013年

关键词：

Software quality; quality assessment; prediction model; machine learning; transparency; FUZZY; INTERPRETABILITY; SYSTEMS;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Quantification of parameters affecting the software quality is one of the important aspects of research in the field of software engineering. In this paper, we present a comprehensive literature survey of prominent quality molding studies. The survey addresses two views: (1) quantification of parameters affecting the software quality; and (2) using machine learning techniques in predicting the software quality. The paper concludes that, model transparency is a common shortcoming to all the surveyed studies.

引用

页数：4

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