Design and Analysis of Improvised Genetic Algorithm with Particle Swarm Optimization for Code Smell Detection
James Benedict Felix S1, Viji Vinod2

1James Benedict Felix S*, Research Scholar, Bharathiar University, Coimbatore, India. Email:
2Viji Vinod, Professor & Head, Department of Computer Applications, Dr.M.G.R. Educational and Research Institute, University, Chennai, India.

Manuscript received on October 12, 2019. | Revised Manuscript received on 22 October, 2019. | Manuscript published on November 10, 2019. | PP: 5327-5330 | Volume-9 Issue-1, November 2019. | Retrieval Number: A5328119119/2019©BEIESP | DOI: 10.35940/ijitee.A5328.119119
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Abstract: 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.
Keywords: Code Smell, Genetic Algorithm, Improvised Genetic Algorithm
Scope of the Article: Algorithm Engineering