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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Czibula, I.G. Czibula, G. Guran, A.M. |
| Copyright Year | 2009 |
| Abstract | Abstract data types (ADTs) represent the core for any software application, and a proper use of them is an essential requirement for developing a robust and efficient system. Moreover, a proper instantiation of a data structure that implements an abstract data type can greatly impact the performance of the system. In this paper we propose a learning approach for the dynamic configuration of data structures instances in a software system. In order to adapt a data structure to the system’s current execution context, a neural network will be used and an agent based system is proposed. We experimentally evaluate our system on a case study, emphasizing the advantages of the proposed approach. |
| Starting Page | 341 |
| Ending Page | 347 |
| File Size | 189142 |
| Page Count | 7 |
| File Format | |
| ISBN | 9781424459100 |
| e-ISBN | 9781424459117 |
| DOI | 10.1109/SYNASC.2009.25 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-09-26 |
| Publisher Place | Romania |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Scientific computing Software algorithms data structure Data structures Application software machine learning Computer science Supervised learning Neural networks Machine learning Software systems Robustness agent-based system |
| Content Type | Text |
| Resource Type | Article |
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