Using Neural Networks In Software Metrics
Software metrics provide effective methods for characterizing software. Metrics have traditionally been composed through the definition of an equation, but this approach is limited by the fact that all the interrelationships among all the parameters be fully understood. Derivation of a polynomial providing the desired characteristics is a substantial challenge. In this paper instead of using conventional methods for obtaining software metrics, we will try to use a neural network for that purpose. Experiments performed in the past on two widely known metrics, McCabe and Halstead, indicate that this approach is feasible.
Volume (Year): 1 (2007)
Issue (Month): 1 (Winter)
|Contact details of provider:|| Postal: Bd.Expozitiei 1B, Bucuresti, Sector 1, Etaj 5, 012101|
Web page: http://www.rau.ro/
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:rau:jisomg:v:1:y:2007:i:1:p:80-85. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Alex Tabusca)
If references are entirely missing, you can add them using this form.