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https://hdl.handle.net/20.500.14279/13812| Τίτλος: | Investigating the predictability of empirical software failure data with artificial neural networks and hybrid models | Συγγραφείς: | Koutsimpelas, Alexandros Andreou, Andreas S. |
Major Field of Science: | Engineering and Technology | Field Category: | Electrical Engineering - Electronic Engineering - Information Engineering | Λέξεις-κλειδιά: | Evolutionary algorithms;Classification (of information);Neural networks | Ημερομηνία Έκδοσης: | Ιου-2006 | Πηγή: | 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 | Περίληψη: | 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 |
| Εμφανίζεται στις συλλογές: | Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation |
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