Predicting Reliability of Object-Oriented Systems Using a Neural Network

Authors

  • A. Budur Department of Computer Science, Faculty of Mathematics and Computer Science, Babes-Bolyai University, Cluj-Napoca, Romania
  • C. Șerban Department of Computer Science, Faculty of Mathematics and Computer Science, Babes-Bolyai University, Cluj-Napoca, Romania
  • A. Vescan Department of Computer Science, Faculty of Mathematics and Computer Science, Babes-Bolyai University, Cluj-Napoca, Romania

DOI:

https://doi.org/10.24193/subbi.2019.2.05

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 Chidamber 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 reliability using a neural network method.

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Published

2019-12-07

Issue

Vol. 64 No. 2 (2019)

Section

Articles

License

Transfer of copyright agreement: When the article is accepted for publication, I, as the author and the representative of the coauthors, hereby agree to transfer to Studia Universitatis Babes-Bolyai, Series Informatica, all rights, including those pertaining to electronic forms and transmissions, under existing copyright laws, except for the following, which the authors specifically retain: the authors can use the material however they want as long as it fits the NC ND terms of the license. The authors have all rights for reuse according to the below license.