Application of Neural Networks for Software Quality Prediction using Object-Oriented Metrics

Thwin, Mie Mie Thet; Quah, Tong Seng

dc.contributor.author	Thwin, Mie Mie Thet
dc.contributor.author	Quah, Tong Seng
dc.date.accessioned	2020-03-16T17:00:45Z
dc.date.available	2020-03-16T17:00:45Z
dc.date.issued	2005-05
dc.identifier.citation	https://doi.org/10.1016/j.jss.2004.05.001	en_US
dc.identifier.issn	0164-1212
dc.identifier.uri	https://onlineresource.ucsy.edu.mm/handle/123456789/2509
dc.description.abstract	This paper presents the application of neural networks in software quality estimation using object-oriented metrics. In this paper, two kinds of investigation are performed. The first on predicting the number of defects in a class and the second on predicting the number of lines changed per class. Two neural network models are used, they are Ward neural network and General Regression neural network (GRNN). Object-oriented design metrics concerning inheritance related measures, complexity measures, cohesion measures, coupling measures and memory allocation measures are used as the independent variables. GRNN network model is found to predict more accurately than Ward network model.	en_US
dc.language.iso	en	en_US
dc.publisher	Journal of Systems and Software	en_US
dc.relation.ispartofseries	;Vol. 76, Issue 2, pp. 147-156
dc.title	Application of Neural Networks for Software Quality Prediction using Object-Oriented Metrics	en_US
dc.type	Article	en_US