Automated framework for classification and selection of software design patterns

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

34 Scopus Citations
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Original languageEnglish
Pages (from-to)1-20
Journal / PublicationApplied Soft Computing Journal
Online published9 Nov 2018
Publication statusPublished - Feb 2019


Though, Unified Modeling Language (UML), Ontology, and Text categorization approaches have been used to automate the classification and selection of design pattern(s). However, there are certain issues such as time and effort for formal specification of new patterns, system context-awareness, and lack of knowledge which needs to be addressed. We propose a framework (i.e. Three-phase method) to discuss these issues, which can aid novice developers to organize and select the correct design pattern(s) for a given design problem in a systematic way. Subsequently, we propose an evaluation model to gauge the efficacy of the proposed framework via certain unsupervised learning techniques. We performed three case studies to describe the working procedure of the proposed framework in the context of three widely used design pattern catalogs and 103 design problems. We find the significant results of Fuzzy c-means and Partition Around Medoids (PAM) as compared to other unsupervised learning techniques. The promising results encourage the applicability of the proposed framework in terms of design patterns organization and selection with respect to a given design problem.

Research Area(s)

  • Design patterns, Design problems, Feature selection, Supervised learning, Text categorization, Unsupervised learning