TY - GEN
T1 - A framework for ranking of software design patterns
AU - Hussain, Shahid
AU - Keung, Jacky
AU - Khan, Arif Ali
N1 - Funding Information:
This research is supported by the City University of Hong Kong research funds (Project No. 7004683, 7004474 and 7200354).
Publisher Copyright:
© Springer International Publishing AG 2018.
PY - 2018
Y1 - 2018
N2 - Several software design patterns have been familiarized either in canonical or as variant solutions in order to solve a problem. Novice designers mostly adopt patterns without considering their ground reality and relevancy with design problems, which may cause to increase the development and maintenance efforts. In order to realize the ground reality and to automate the selection process, the existing automated systems for the selection of design patterns either need formal specification or precise learning through training the numerous classifiers. In order to address this issue, we propose an approach on the base of a supervised learning technique named ‘Learning to Rank’, to rank the design patterns with respect to text similarity with the description of the given design problems. Subsequently, we also propose an evaluation model in order to assess the effectiveness of the proposed approach. We evaluate the effectiveness of the proposed approach in the context of several design pattern collections and relevant design problems. The promising experimental results indicate the applicability of the proposed approach.
AB - Several software design patterns have been familiarized either in canonical or as variant solutions in order to solve a problem. Novice designers mostly adopt patterns without considering their ground reality and relevancy with design problems, which may cause to increase the development and maintenance efforts. In order to realize the ground reality and to automate the selection process, the existing automated systems for the selection of design patterns either need formal specification or precise learning through training the numerous classifiers. In order to address this issue, we propose an approach on the base of a supervised learning technique named ‘Learning to Rank’, to rank the design patterns with respect to text similarity with the description of the given design problems. Subsequently, we also propose an evaluation model in order to assess the effectiveness of the proposed approach. We evaluate the effectiveness of the proposed approach in the context of several design pattern collections and relevant design problems. The promising experimental results indicate the applicability of the proposed approach.
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U2 - 10.1007/978-3-319-61566-0_20
DO - 10.1007/978-3-319-61566-0_20
M3 - Conference contribution
AN - SCOPUS:85026322140
SN - 9783319615653
T3 - Advances in Intelligent Systems and Computing
SP - 205
EP - 215
BT - Complex, Intelligent, and Software Intensive Systems - Proceedings of the 11th International Conference on Complex, Intelligent, and Software Intensive Systems, CISIS 2017
A2 - Barolli, Leonard
A2 - Terzo, Olivier
PB - Springer Verlag
T2 - 11th International Conference on Complex, Intelligent, and Software Intensive Systems, CISIS 2017
Y2 - 10 July 2017 through 12 July 2017
ER -