Paper Info
Title
A methodology to rank the design patterns on the base of text relevancy
Abstract
Several software design patterns have cataloged either with canonical or as variants to solve a recurring design problem. However, novice designers mostly adopt patterns without considering their ground reality and relevance to design problems, which causes to increase the development and maintenance efforts. The existing automated systems to select the design patterns need either high computing effort and time for the formal specification or precise learning through the training of several classifiers with large sample size to select the right design patterns realized on the base of their ground reality. In order to discuss this issue, we propose a method on the base of a supervised learning technique named ‘Learning to Rank’, to rank the design patterns via the text relevancy with the description of the given design problems. Subsequently, we also propose an evaluation model to assess the effectiveness of the proposed method. We evaluate the efficacy of the proposed method in the context of several design pattern collections and relevant design problems. The promising experimental results indicate the applicability of the proposed method as a recommendation system to select the right design pattern(s).
Year
DOI
Venue
2019
10.1007/s00500-019-03882-y
Soft Computing
Keywords
Field
DocType
Software design patterns, Text mining, Learning to rank, Performance, Classification
Recommender system,Learning to rank,Software design,Computer science,Software design pattern,Formal specification,Supervised learning,Artificial intelligence,Machine learning,Sample size determination,Design pattern
Journal
Volume
Issue
ISSN
23
24
1433-7479
Citations 
PageRank 
References 
0
0.34
19
Authors
8
Name
Order
Citations
PageRank
Shahid Hussain118942.08
Jacky Keung29712.44
Mohammad Khalid Sohail300.34
Arif Ali Khan44717.15
M. Ilahi55710.91
Ghufran Ahmad600.34
Muhammad Rafiq Mufti786.42
Muhammad Asim Noor800.34