DocumentCode
630444
Title
Machine Learning-Based Software Quality Prediction Models: State of the Art
Author
Al-Jamimi, Hamdi A. ; Ahmed, Mariwan
Author_Institution
Inf. & Comput. Sci. Dept., King Fahd Univ. of Pet. & Miner., Dhahran, Saudi Arabia
fYear
2013
fDate
24-26 June 2013
Firstpage
1
Lastpage
4
Abstract
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.
Keywords
learning (artificial intelligence); software quality; machine learning; quality molding; software engineering; software quality prediction model; Biological system modeling; Fuzzy logic; Object oriented modeling; Predictive models; Software engineering; Software quality;
fLanguage
English
Publisher
ieee
Conference_Titel
Information Science and Applications (ICISA), 2013 International Conference on
Conference_Location
Suwon
Print_ISBN
978-1-4799-0602-4
Type
conf
DOI
10.1109/ICISA.2013.6579473
Filename
6579473
Link To Document