IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Code Clone Detection and Analysis in Open Source Applications

Code Clone Detection and Analysis in Open Source Applications
View Sample PDF
Author(s): Al-Fahim Mubarak-Ali (Universiti Teknologi Malaysia, Malaysia), Shahida Sulaiman (Universiti Teknologi Malaysia, Malaysia), Sharifah Mashita Syed-Mohamad (Universiti Sains Malaysia, Malaysia) and Zhenchang Xing (Nanyang Technological University, Singapore)
Copyright: 2014
Pages: 16
Source title: Handbook of Research on Emerging Advancements and Technologies in Software Engineering
Source Author(s)/Editor(s): Imran Ghani (Universiti Teknologi Malaysia, Malaysia), Wan Mohd Nasir Wan Kadir (Universiti Teknologi Malaysia, Malaysia) and Mohammad Nazir Ahmad (Universiti Teknologi Malaysia, Malaysia)
DOI: 10.4018/978-1-4666-6026-7.ch022

Purchase

View Code Clone Detection and Analysis in Open Source Applications on the publisher's website for pricing and purchasing information.

Abstract

Code clone is a portion of codes that contains some similarities in the same software regardless of changes made to the specific code such as removal of white spaces and comments, changes in code syntactic, and addition or removal of code. Over the years, many approaches and tools for code clone detection have been proposed. Most of these approaches and tools have managed to detect and analyze code clones that occur in large software. In this chapter, the authors aim to provide a comparative study on current state-of-the-art in code clone detection approaches and models together with their corresponding tools. They then perform an empirical evaluation on the selected code clone detection tool and organize the large amount of information in a more systematic way. The authors begin with explaining background concepts of code clone terminology. A comparison is done to find out strengths and weaknesses of existing approaches, models, and tools. Based on the comparison done, they then select a tool to be evaluated in two dimensions, which are the amount of detected clones and run time performance of the tool. The result of the study shows that there are various terminologies used for code clone. In addition, the empirical evaluation implies that the selected tool (enhanced generic pipeline model) gives a better code clone output and runtime performance as compared to its generic counterpart.

Related Content

Kamalendu Pal. © 2020. 22 pages.
Chetna Gupta, Surbhi Singhal, Astha Kumari. © 2020. 12 pages.
Sudha Srinivasan, D. S. Chauhan. © 2020. 16 pages.
Priyanka Chandani, Chetna Gupta. © 2020. 30 pages.
Chamundeswari Arumugam, Srinivasan Vaidyanathan. © 2020. 13 pages.
Varun Gupta, Aditya Raj Gupta, Utkarsh Agrawal, Ambika Kumar, Rahul Verma. © 2020. 15 pages.
Vimaladevi M., Zayaraz G.. © 2020. 25 pages.
Body Bottom