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Examining deep learning’s capability to spot code smells: a systematic literature review

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Abstract

Code smells violate software development principles that make the software more prone to errors and changes. Researchers have developed code smell detectors using manual and semi-automatic methods to identify these issues. However, three key challenges have limited the practical use of these detectors: developers’ subjective perceptions of code smells, lack of consensus in the detection process, and difficulty in setting appropriate detection thresholds. While code smell detection using machine learning has progressed significantly, there still appears to be a gap in understanding the effective utilization of deep learning (DL) approaches. This paper aims to review and identify current methods for code smell detection using DL techniques. A systematic literature review is conducted on 35 primary studies from a collection of 8739 publications between 2013 and the present. The analysis reveals that common code smells detected include Feature Envy, God Classes, Long Methods, Complex Classes, and Large Classes. The most popular DL algorithms used are Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN), often combined with other techniques for better results. Algorithms that train models on large datasets with fewer independent variables demonstrate exemplary performance. The paper also highlights open issues and provides guidelines for future metric identification and selection research.

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Author information

Authors and Affiliations

  1. Delhi Technological University, Delhi, India

    Ruchika Malhotra & Bhawna Jain

  2. Indira Gandhi Delhi Technical University for Women, Delhi, India

    Bhawna Jain

  3. University of Michigan-Flint College of Innovation & Technology, Michigan, USA

    Marouane Kessentini

Authors
  1. Ruchika Malhotra
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  2. Bhawna Jain
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  3. Marouane Kessentini
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Correspondence to Bhawna Jain.

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Appendices

Appendix A. List of Metrics with their Definitions used in this Study

Acronym

Definition

AMW

Afferent Method and Weighted Method

ATFD

Access to Foreign Data

BUR

Buse Readability Score

CBO

Coupling Between Objects

CDISP

Class Dependency Metric

CINT

Coupling Intensity

CM

Coupling Between Objects

CYCLO

Cyclomatic Complexity

DIT

Depth of Inheritance Tree

FDP

Foreign Data Providers

LAA

Locality of Attribute Accesses

LB

Basic Lack of Cohesion in Methods

LCOM

Lack of Cohesion in Methods

LOC

Lines Of Code

MAXNESTING

Maximum Nesting Level

NOAM

Number of Accessed Members

NOAV

Number of Accessed Variables

NOC

Number of Children

NOM

Number of Methods

NOP

Number of Parents

PAR

Number of Parameters

SDC

Size of Data Context

SEC

Signal Event Coupling

TCC

Tight Class Cohesion

WMC

Weighted Methods Count

Appendix B. List of Primary Studies Selected for this SLR

ID

Title

References

S1

Fusion of deep convolutional and LSTM recurrent neural networks for automated detection of code smells

[39]

S2

Deep Multimodal Architecture for Detection of Long Parameter List and Switch Statements using DistilBERT

[40]

S3

DACOS-A Manually Annotated Dataset of Code Smells

[41]

S4

Improving the Quality of Open Source Software

[42]

S5

Web Service Anti-patterns Detection Using CNN with Varying Sequence Padding Size

[43]

S6

Using Word Embedding and Convolution Neural Network for Bug Triaging by Considering Design Flaws

[44]

S7

Code Smell Detection Using Ensemble Machine Learning Algorithms

[45]

S8

Application of Deep Learning models for Code Smell Prediction

[46]

S9

DeleSmell: Code smell detection based on deep learning and latent semantic analysis

[47]

S10

Feature Envy Detection with Deep Learning and Snapshot Ensemble

[48]

S11

How to improve deep learning for software analytics: (a case study with code smell detection)

[49]

S12

Multi-Label Code Smell Detection with Hybrid Model based on Deep Learning

[50]

S13

Detecting Code Smells with AI: a Prototype Study

[51]

S14

The automation of the detection of large class bad smell by using genetic algorithm and deep learning

[52]

S15

A machine and deep learning analysis among SonarQube rules, product, and process metrics for fault prediction

[53]

S16

Multi-granularity code smell detection using deep learning method based on abstract syntax tree

[54]

S17

An Empirical Study on Predictability of Software Code Smell Using Deep Learning Models

[55]

S18

Two-Pass Technique for Clone Detection and Type Classification Using Tree-Based Convolution Neural Network

[56]

S19

FCCA: Hybrid Code Representation for Functional Clone Detection Using Attention Networks

[57]

S20

MARS: Detecting brain class/method code smell based on metric-attention mechanism and residual network

[58]

S21

Code smell detection by deep direct-learning and transfer-learning

[59]

S22

Deep Learning Based Code Smell Detection

[60]

S23

Feature Envy Detection based on Bi-LSTM with Self-Attention Mechanism

[61]

S24

Deep Representation Learning for Code Smells Detection using Variational Auto-Encoder

[62]

S25

Deep Learning Anti-Patterns from Code Metrics History

[63]

S26

Deep semantic-Based Feature Envy Identification

[64]

S27

Detecting code smells using deep learning

[65]

S28

On the feasibility of transfer-learning code smells using deep learning

[66]

S29

Deep learning based feature envy detection

[67]

S30

A hybrid approach to detect code smells using deep learning

[68]

S31

Cclearner: A deep learning-based clone detection approach

[69]

S32

Finding bad code smells with neural network models

[70]

S33

Deep learning code fragments for code clone detection

[71]

S34

Some code smells have a significant but small effect on faults

[72]

S35

What you like in design use to correct bad-smell

[18]

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Malhotra, R., Jain, B. & Kessentini, M. Examining deep learning’s capability to spot code smells: a systematic literature review. Cluster Comput 26, 3473–3501 (2023). https://doi.org/10.1007/s10586-023-04144-1

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