Application of Neural Networks for Estimating Software Maintainability 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	2020-03-13T09:46:46Z
dc.date.available	2020-03-13T09:46:46Z
dc.date.issued	2003-07-01
dc.identifier.isbn	1891706128
dc.identifier.uri	http://onlineresource.ucsy.edu.mm/handle/123456789/2505
dc.description.abstract	This paper presents the application of neural networks in software maintainability estimation using objectoriented metrics. Maintenance effort can be measured as the number of lines changed per class. In this paper, the number of lines changed per class (modification volume) is predicted using Ward neural network and General Regression neural network (GRNN). Object-oriented design metrics concerning with inheritance related measures, complexity measures, cohesion measures, coupling measures and size measures are applied in this study. Principal components, which are derived from these object-oriented metrics, are used as independent variables.	en_US
dc.language.iso	en	en_US
dc.publisher	Proceedings of the Fifteenth International Conference on Software Engineering & Knowledge Engineering (SEKE'2003)	en_US
dc.relation.ispartofseries	;pp. 69-73
dc.title	Application of Neural Networks for Estimating Software Maintainability Using Object-Oriented Metrics	en_US
dc.type	Article	en_US