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Adaptive recurrent neural network for software defect prediction with the aid of quantum theory- particle swarm optimization

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Abstract

With the proliferation of software programs, predicting defects has become a big concern. Therefore, to overcome this challenge, this research introduces a new Optimized Deep Learning model. The software defect is predicted using the new Adaptive Recurrent Neural network (ARNN), wherein the hyper-parameters (weight) function is fine-tuned using the new Levy-Flight Integrated Cuckoo Search Optimization (LICSO) model to accurately predict the defects. First, the data is pre-processed via box-cox transformation. The outcomes of the pre-processed data are then subjected to a Feature Selection technique, wherein the relevant features are selected using the new Quantum Theory-Particle Swarm Optimization (QPSO-FS). Finally, ARNN is utilized to predict the software defects. To validate the performance of the proposed approach evaluation metrics are considered such as detection Accuracy, Precision, Recognition error, Sensitivity, Specificity, F1-Score, and Processing time are evaluated and tested. As per the acquired results, the projected model outperforms the existing models. The projected model has recorded the highest accuracy level as 96.48%.

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Data availability

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

Abbreviations

ARNN:

Adaptive Recurrent Neural Network

COA:

Cuckoo Optimization Algorithm

QPSO-FS:

Quantum Theory-Particle Swarm Optimization

SDP:

Software Defect Prediction

CPDP:

Cross-Project Defect Prediction

WPDP:

Within-Project Defect Prediction

FDNB:

Feature Dependent Naive Bayes

DBNN:

Deep Belief Neural Network

MLACO:

Multi-Label based Ant Colony Optimization

KPCA:

Kernel Principal Component Analysis

CNN:

Convolutional Neural Network

LICSO:

Levy-Flight Integrated Cuckoo Search Optimization

AST:

Abstract Syntax Tree

SDNN:

Siamese Dense Neural Networks

HMOCS:

Hybrid Multi-Objective Cuckoo Search with Dynamical Local Search

FNR:

False Negative Rate

DNN:

Deep Neural Network

FPR:

False Positive Rate

TSE:

Two-Stage Ensemble

ANN:

Artificial Neural Network

FeSCH:

Feature Selection Using Clusters of Hybrid-Data

SVM:

Support Vector Machine

RF :

Random Forest

L :

Database

I :

Number of the defect and non-defect data

m :

Number of data

C n :

Pre-processed data

I n :

Input data

\({V}_j^u\) :

The velocity of the particles

\({Y}_j^u\) :

Position of the particles

N :

Number of particles

w :

Inertia weight

r j :

Local attracter of the particle

pd:

Probability of detection

E (t, a) :

Classifier’s output

γ :

Uniform distribution

R(t, τ):

Characteristic scale

P b :

Generation of new solution

n :

Arbitrary units

ϑ and f :

Arbitrary values

λ:

Weight set

δ (t) :

Activation function

nbest :

Average optimal position

gbest :

Global search space

pf:

False-positive rate

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Authors and Affiliations

  1. Computer Science and Engineering, Noorul Islam Centre for Higher Education, Thuckalay, Kumarakovil, India

    A. J. Anju & J. E. Judith

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Anju, A.J., Judith, J.E. Adaptive recurrent neural network for software defect prediction with the aid of quantum theory- particle swarm optimization. Multimed Tools Appl 82, 16257–16278 (2023). https://doi.org/10.1007/s11042-022-14065-7

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