Investigating the predictability of empirical software failure data with artificial neural networks and hybrid models

Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/13812

Title:	Investigating the predictability of empirical software failure data with artificial neural networks and hybrid models
Authors:	Koutsimpelas, Alexandros Andreou, Andreas S.
Major Field of Science:	Engineering and Technology
Field Category:	Electrical Engineering - Electronic Engineering - Information Engineering
Keywords:	Evolutionary algorithms;Classification (of information);Neural networks
Issue Date:	Jun-2006
Source:	Artificial Intelligence Applications and Innovations. AIAI 2006. IFIP International Federation for Information Processing, vol 204, pp. 524-532
Volume:	204
Conference:	International Conference on Artificial Intelligence Applications and Innovations
Abstract:	Software failure and software reliability are strongly related concepts. Introducing a model that would perform successful failure prediction could provide the means for achieving higher software reliability and quality. In this context, we have employed artificial neural networks and genetic algorithms to investigate whether software failure can be accurately modeled and forecasted based on empirical data of real systems. © 2006 International Federation for Information Processing.
ISBN:	978-0-387-34224-5
DOI:	10.1007/0-387-34224-9_61
Rights:	© International Federation for Information Processing
Type:	Conference Papers
Affiliation :	University of Cyprus
Publication Type:	Peer Reviewed
Appears in Collections:	Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation

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