Rehef, K.K.; Abbas, A.S. Improving Code Smell Detection Using Deep Stacked Autoencoder. Preprints2024, 2024031848. https://doi.org/10.20944/preprints202403.1848.v1
APA Style
Rehef, K.K., & Abbas, A.S. (2024). Improving Code Smell Detection Using Deep Stacked Autoencoder. Preprints. https://doi.org/10.20944/preprints202403.1848.v1
Chicago/Turabian Style
Rehef, K.K. and Ahmed S. Abbas. 2024 "Improving Code Smell Detection Using Deep Stacked Autoencoder" Preprints. https://doi.org/10.20944/preprints202403.1848.v1
Abstract
The term "code smell" refers to an indication of a problem with the quality of source code. Numerous studies have been conducted to identify problematic features in source code. Initially, the focus was on utilizing metric-based and heuristic-based approaches. In recent years, however, there has been a shift towards using machine learning and deep learning (DL) techniques for smell detection. Nevertheless, the current algorithms are still considered to be in the early stages of development. Recognizing the challenges associated with identifying smells using DL methods, both academics and software developers have made efforts to address these obstacles. This work involves constructing and evaluating new DL models for code smell detection. Two models are built upon stacked autoencoders, employing a hybrid architecture that combines bidirectional long short-term memory and convolutional neural network components.
Keywords
machine learning; deep learning; stack auto encoder; code smell detection; auto encoder.
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
