Please use this identifier to cite or link to this item: http://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: Proceedings of the 7th International Symposium on Search Based Software Engineering, 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: 7th International Symposium on Search Based Software Engineering (SSBSE 2015) (05 Sep 2015 - 07 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
RMID: 0030041766
DOI: 10.1007/978-3-319-22183-0_28
Appears in Collections:Computer Science publications

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