Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/108325
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Type: | Conference paper |
Title: | Introducing learning mechanism for class responsibility assignment problem |
Author: | Xu, Y. Liang, P. Babar, M. |
Citation: | Lecture Notes in Artificial Intelligence, 2015, vol.9275, pp.311-317 |
Publisher: | Springer |
Issue Date: | 2015 |
Series/Report no.: | Lecture Notes in Computer Science (LNCS, vol. 9275) |
ISBN: | 9783319221823 |
ISSN: | 0302-9743 1611-3349 |
Conference Name: | International Symposium on Search-Based Software Engineering (SSBSE) (5 Sep 2015 - 7 Sep 2015 : Bergamo, Italy) |
Statement of Responsibility: | Yongrui Xu, Peng Liang, and Muhammad Ali Babar |
Abstract: | Assigning responsibilities to classes is a vital task in object-oriented design, which has a great impact on the overall design of an application. However, this task is not easy for designers due to its complexity. Though many automated approaches have been developed to help designers to assign responsibilities to classes, none of them considers extracting the design knowledge (DK) about the relations between responsibilities in order to adapt designs better against design problems. To address the issue, we propose a novel Learning-based Genetic Algorithm (LGA) for the Class Responsibility Assignment (CRA) problem. In the proposed algorithm, a learning mechanism is introduced to extract DK about which responsibilities have a high probability to be assigned to the same class, and the extracted DK is employed to improve the design qualities of generated solutions. An experiment was conducted, which shows the effectiveness of the proposed approach. |
Keywords: | CRA problem, data mining, genetic algorithm, The Baldwin effect |
Rights: | © Springer International Publishing Switzerland 2015 |
DOI: | 10.1007/978-3-319-22183-0_28 |
Published version: | http://dx.doi.org/10.1007/978-3-319-22183-0_28 |
Appears in Collections: | Aurora harvest 8 Computer Science publications |
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RA_hdl_108325.pdf Restricted Access | Restricted Access | 171.98 kB | Adobe PDF | View/Open |
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