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Authors: Mouna Hadj-Kacem and Nadia Bouassida

Affiliation: Mir@cl Laboratory, Tunisia

ISBN: 978-989-758-300-1

Keyword(s): Hybrid Approach, Deep Learning, Auto-encoder, Artificial Neural Networks, Code Smell Detection.

Abstract: The detection of code smells is a fundamental prerequisite for guiding the subsequent steps in the refactoring process. The more the detection results are accurate, the more the performance of the refactoring on the software is improved. Given its influential role in the software maintenance, this challenging research topic has so far attracted an increasing interest. However, the lack of consensus about the definition of code smells in the literature has led to a considerable diversity of the existing results. To reduce the confusion associated with this lack of consensus, there is a real need to achieve a deep and consistent representation of the code smells. Recently, the advance of deep learning has demonstrated an undeniable contribution in many research fields including the pattern recognition issues. In this paper, we propose a hybrid detection approach based on deep Auto-encoder and Artificial Neural Network algorithms. Four code smells (God Class, Data Class, Feature Envy and Long Method) are the focus of our experiment on four adopted datasets that are extracted from 74 open source systems. The values of recall and precision measurements have demonstrated high accuracy results. (More)

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Paper citation in several formats:
Hadj-Kacem, M. and Bouassida, N. (2018). A Hybrid Approach To Detect Code Smells using Deep Learning.In Proceedings of the 13th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE, ISBN 978-989-758-300-1, pages 137-146. DOI: 10.5220/0006709801370146

@conference{enase18,
author={Mouna Hadj{-}Kacem. and Nadia Bouassida.},
title={A Hybrid Approach To Detect Code Smells using Deep Learning},
booktitle={Proceedings of the 13th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE,},
year={2018},
pages={137-146},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006709801370146},
isbn={978-989-758-300-1},
}

TY - CONF

JO - Proceedings of the 13th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE,
TI - A Hybrid Approach To Detect Code Smells using Deep Learning
SN - 978-989-758-300-1
AU - Hadj-Kacem, M.
AU - Bouassida, N.
PY - 2018
SP - 137
EP - 146
DO - 10.5220/0006709801370146

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