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- Title
PREDICTING RELIABILITY OF OBJECT-ORIENTED SYSTEMS USING A NEURAL NETWORK.
- Authors
BUDUR, ALISA; şERBAN, CAMELIA; VESCAN, ANDREEA
- Abstract
One of the most important quality attributes of computer systems is reliability, which addresses the ability of the software to perform its required function under stated conditions for a stated period of time. The paper aim is twofold. Firstly, the proposed approach explores how to define a metric to qualify the sub-aspects comprised in ISO 25010 regarding reliability as maturity and availability. Secondly, we investigate to what extent the internal structure of the system quantified by the Chi- damber and Kemerer (CK) metrics may be used to predict reliability. The approach for prediction is a feed-forward neural network with back-propagation learning. The results indicate that CK metrics are promising in predicting re- liability using a neural network method.
- Publication
Studia Universitatis Babes-Bolyai, Informatica, 2019, Vol 64, Issue 2, p65
- ISSN
1224-869X
- Publication type
Academic Journal
- DOI
10.24193/subbi.2019.2.05