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 :
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