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Empirical Software Engineering

Erschienen in:

01.05.2023

Deep learning approaches for bad smell detection: a systematic literature review

verfasst von: Amal Alazba, Hamoud Aljamaan, Mohammad Alshayeb

Erschienen in: Empirical Software Engineering | Ausgabe 3/2023

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Abstract

Context

Bad smells negatively impact software quality metrics such as understandability, reusability, and maintainability. Reduced costs and enhanced software quality can be achieved through accurate bad smell detection.

Objective

This review aims to summarize and synthesize the studies that used deep learning (DL) techniques for bad smell detection. Given the rapid growth of DL techniques, we believe that reviewing and analyzing the current body of knowledge would facilitate the development of new techniques and help researchers identify research gaps in this area.

Method

We followed a systematic approach to identify 67 studies on DL-based bad smell detection published until October 2021. We collected and analyzed quantitative and qualitative data to obtain our results.

Results

Code Clone was the most recurring smell. Supervised learning is the most adopted learning approach for DL-based bad smell detection. Convolutional neural network (CNN), Artificial neural network (ANN), Deep neural network (DNN), Long short-term memory (LSTM), Attention model, and recursive autoencoder (RAE) are the most popularly used DL models. DL models that efficiently detect bad smells, such as Tree-based CNN (TBCNN) and the Abstract syntax tree-based LSTM (AST-LSTM), tend to be specifically designed to encode features for bad smell detection.

Conclusion

Many factors can affect the detection performance of DL models. Although studies exist on DL-based bad smell detection, more works that use other DL models than those already studied are needed. In this SLR, we provide a summary of existing research and recommendations for further research directions on DL-based bad smell detection.

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https://github.com/amalazba/Deep-Learning-Approaches-for-Bad-Smell-Detection-SLR

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Titel: Deep learning approaches for bad smell detection: a systematic literature review
verfasst von: Amal Alazba
Hamoud Aljamaan
Mohammad Alshayeb
Publikationsdatum: 01.05.2023
Verlag: Springer US
Erschienen in: Empirical Software Engineering / Ausgabe 3/2023
Print ISSN: 1382-3256
Elektronische ISSN: 1573-7616
DOI: https://doi.org/10.1007/s10664-023-10312-z

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