Open AccessDetecting Meta-Patterns from Frameworks Using Hybrid Genetic Algorithm
Author(s) -
Tapan Kant,
Manjari Gupta,
Anil Kumar Tripathi,
M. Prakash
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i2.20.16732
Subject(s) - computer science , algorithm , genetic algorithm , metamodeling , sorting algorithm , unified modeling language , precision and recall , data mining , graph , sort , software design pattern , sorting , artificial intelligence , theoretical computer science , machine learning , software engineering , programming language , information retrieval , software
Meta-patterns are a sort of basic object-oriented constructs that have been used to design an object-oriented framework. It has been used to precisely describe possible design pattern of a framework at meta-level to manifest framework hot-spots and its corresponding adaptability. The present study is an attempt to develop a genetic algorithm approach for detecting the types and numbers of meta-patterns. For this purpose we have converted the UML class diagram of object-oriented framework and meta-patterns into directed graph and applied hybrid genetic algorithm. The obtained results from the proposed algorithm are further validated manually with the recall and precision percentage of 86.20 and 80.64 respectively. Overall the study demonstrates the utility of the uniquely proposed algorithm for the near accurate identification of meta-patterns for high reusability. This can be applied on frameworks for assessing the evolution process, documentation of hot-spots and reducing the customization effort.