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A Novel Transfer Learning Method for Code Smell Detection on Heterogeneous Data: A Feasibility Study

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Abstract

Code smell detection has been primarily focused on homogeneous data. However, due to diverse sources of data, in a real-life scenario, the unseen target data on which code smell needs to be predicted may be heterogeneous in feature space representation from source data for which code smells are known. Also, the capability of a state-of-the-art technique of machine learning called “transfer learning” has not been well explored to transfer the knowledge of already known code smells from source data to predict code smells on unseen heterogeneous target data. This paper has examined the feasibility of transfer learning to predict code smells on unseen heterogeneous target data. The paper has proposed a novel method for detecting code smell on heterogeneous data using modified domain invariant transfer kernel learning (DITKL), one of the transfer learning techniques. The experiments were conducted using modified DITKL on six traditional machine learning models on long method and temporary field code smells. Results showed that modified DITKL on Naïve Bayes and the ID3 decision tree outperformed others for long method code smell, and modified DITKL on Multilayer Perceptron and Naïve Bayes performed well for temporary field code smell. The proposed method can be quite useful to detect code smells in unseen heterogeneous data when a tool or expert knowledge cannot be applied to detect a code smell due to characteristics of the unseen data. It can also help in establishing benchmark data for code smells.

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Authors and Affiliations

  1. Department of Computer Science & Engineering and Information Technology, Jaypee Institute of Information Technology, Noida, India

    Ruchin Gupta & Sandeep Kumar Singh

  2. Department of Information Technology, Kiet Group of Institutions, Ghaziabad, Delhi-NCR, India

    Ruchin Gupta

Authors
  1. Ruchin Gupta
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Corresponding author

Correspondence to Ruchin Gupta.

Ethics declarations

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Ethical Approval

The experimental studies in the paper use datasets that do not contain any sensitive or private information because the main aim of this research is conceptual and methodological advancements.

Informed Consent

Not relevant. This research does not include any human subjects.

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This article is part of the topical collection “Advanced Computing and Data Sciences” guest edited by Mayank Singh, Vipin Tyagi and P.K. Gupta.

Appendices

Appendix A

List of Abbreaviations

SI no.

Symbol

Description of the symbol

1

Ds

Source domain

2

DT

Target domain

3

Xʹ

Target feature space

4

X

Source feature space

5

P(Xʹ)

Marginal probability distribution of target feature space

6

P(X)

Marginal probability distribution of source feature space

7

TS

Source task

8

TT

Target task

9

Y

Source label set

10

Y’

Target label set

11

P(Y|X)

Conditional probability distribution of source

12

P(Yʹ|Xʹ)

Conditional probability distribution of target

13

xi

ith instance of X

14

f (x)

Predictive function

15

x

x is an instance of X

16

y

y is a label of Y

17

DITKL

Domain Invariant Transfer Kernel Learning

18

MDITKL

Modified Domain Invariant Transfer Kernel Learning

19

SVM

Support vector machine

20

k

Kernel

21

ζ

Eigen-damping factor

22

AUC

Area under an ROC curve

Appendix B

Hyperparameters for traditional machine learning models

SI no.

Name of algorithm

Used modules and their parameters of python sklearn

1

Random forest

param_grid1 = [ {'n_estimators': [1,10,100,100]}] GridSearchCV(estimator = RandomForestClassifier(n_estimators = 100), param_grid = param_grid1, cv = 3, verbose = True, n_jobs = -1)

RandomForestClassifier(best_c)

2

Decision tree (C4.5)

DecisionTreeClassifier()

3

Decision Tree (ID3)

Id3Estimator (gain_ratio = True, prune = True)

4

Naïve Bayes

GaussianNB()

5

Multilayer perceptron

param_grid1 = [ {'solver': ['lbfgs', 'sgd', 'adam']}] GridSearchCV(estimator = MLPClassifier(solver = 'lbfgs', alpha = 1e-5,hidden_layer_sizes = (8, 8), random_state = 1), param_grid = param_grid1, cv = 5,verbose = True, n_jobs = -1)

MLPClassifier(solver = best_c, alpha = 1e-5,hidden_layer_sizes = (8, 8), random_state = 1)

6

KNeighborsClassifier

param_grid1 = [{'n_neighbors': [1,2,3,4,5,6]}] GridSearchCV(estimator = KNeighborsClassifier(), param_grid = param_grid1, cv = 5,verbose = True, n_jobs = -1)

Appendix C

Snapshot of dataset 3

figure b

Snapshot of dataset 5

figure c

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Gupta, R., Singh, S.K. A Novel Transfer Learning Method for Code Smell Detection on Heterogeneous Data: A Feasibility Study. SN COMPUT. SCI. 4, 749 (2023). https://doi.org/10.1007/s42979-023-02157-6

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