Open AccessDesign and Analysis of Improvised Genetic Algorithm with Particle Swarm Optimization for Code Smell Detection
Author(s) -
J Benedict,
Viji Vinod
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.a5328.119119
Subject(s) - code smell , computer science , particle swarm optimization , euclidean distance , software , metaheuristic , code (set theory) , genetic algorithm , algorithm , software development , static program analysis , data mining , artificial intelligence , software quality , programming language , machine learning , set (abstract data type)
Software development phase is very important in the Software Development Life Cycle. Software maintenance is a difficult process if code smells exist in the code. The poor design of code development is called code smells. The code smells are identified by various tools using various approaches. Many code smell approaches are rule based. The rule based approaches are based on trial and error method. Genetic Algorithm is a heuristic Algorithm by Darwin’s Theory. This paper presents a metric based code smell detection approach by Genetic Algorithm with particle swarm optimization based on Euclidean data distance. The Euclidean data distance gives best proximity value between two points. Our approach is evaluated on the three open source projects like JFreeChart v1.0.9, Log4J v1.2.1 and Xerces-J for identifying the eight types of code smells namely Functional Decomposition, Feature Envy, Blob, Long Parameter List, Spaghetti Code, Data Class, Lazy Class, Shotgun Surgery.