Automated framework for classification and selection of software design patterns.

Abstract 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. Highlights • Propose a framework to overcome the limitation of existing automation techniques. • Unsupervised learning techniques are used to exploit the proposed framework. • The aim of proposed framework is the classification and selection of the software design pattern(s). • Propose an evaluation model to assess the effectiveness of the proposed framework. • Present three case studies in different domains such as object-oriented development, real time and security based application. [ABSTRACT FROM AUTHOR]