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Classification framework for faulty-software using enhanced exploratory whale optimizer-based feature selection scheme and random forest ensemble learning

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Abstract

Software Fault Prediction (SFP) is an important process to detect the faulty components of the software to detect faulty classes or faulty modules early in the software development life cycle. In this paper, a machine learning framework is proposed for SFP. Initially, pre-processing and re-sampling techniques are applied to make the SFP datasets ready to be used by ML techniques. Thereafter seven classifiers are compared, namely K-Nearest Neighbors (KNN), Naive Bayes (NB), Linear Discriminant Analysis (LDA), Linear Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), and Random Forest (RF). The RF classifier outperforms all other classifiers in terms of eliminating irrelevant/redundant features. The performance of RF is improved further using a dimensionality reduction method called binary whale optimization algorithm (BWOA) to eliminate the irrelevant/redundant features. Finally, the performance of BWOA is enhanced by hybridizing the exploration strategies of the grey wolf optimizer (GWO) and harris hawks optimization (HHO) algorithms. The proposed method is called SBEWOA. The SFP datasets utilized are selected from the PROMISE repository using sixteen datasets for software projects with different sizes and complexity. The comparative evaluation against nine well-established feature selection methods proves that the proposed SBEWOA is able to significantly produce competitively superior results for several instances of the evaluated dataset. The algorithms’ performance is compared in terms of accuracy, the number of features, and fitness function. This is also proved by the 2-tailed P-values of the Wilcoxon signed ranks statistical test used. In conclusion, the proposed method is an efficient alternative ML method for SFP that can be used for similar problems in the software engineering domain.

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Abbreviations

AAE:

Average absolute error

ABC:

Artificial bee colony

ACO:

Ant colony optimization

ADASYN:

Adaptive synthetic sampling method

ALO:

Ant lion optimizer

ANN:

Artificial neural networks

ARE:

Average Relative Error

ASD:

Agile software development model

AUC:

Area under the curve

BBAT:

Binary bat algorithm

BCS:

Binary cuckoo search

BFFA:

Binary firefly algorithm

BGWO:

Binary grey wolf optimization

BHHO:

Binary harris hawk optimization

BJAYA:

Binary jaya algorithm

BMFO:

Binary moth flame optimization

BN:

Bayesian networks

BQSA:

Binary queuing search algorithm

BWOA:

Binary whale optimization algorithm

CBL:

Case-based learning

COA:

Coyote optimization algorithm

CS:

Chi-square

CSA:

Crow search algorithm

DA:

Dragonfly algorithm

DE:

Differential evolution

DT:

Decision tree

EA:

Evolutionary algorithm

FFA:

Firefly algorithm

FIS:

Fuzzy inference system

FN:

False negative

FP:

False positive

FS:

Feature selection

GA:

genetic algorithm

GBRCR:

Gradient boosting regression-based combination rule

GOA:

Grasshopper optimization algorithm

GP:

Genetic programming

GWO:

Grey wolf optimizer

HHO:

Harris hawks optimization

IG:

information gain

KNN:

K-nearest neighbors

LDA:

Linear discriminant analysis

LR:

Linear regression

LRCR:

linear regression-based combination rule

ML:

Machine learning

MLP:

Multi-layer perceptron

MLR:

Multi-nomial logistic regression

MVO:

multiverse optimizer

NB:

Naive Bayes

OO:

Object-Oriented

PCA:

Principle component analysis

PCC:

Pearson correlation coefficient

PSO:

Particle swarm optimization

QMOOD:

Quality metrics for object-oriented design

RF:

Random forest

ROC:

Receiver operating characteristic

SBWOA:

Binary whale optimization algorithm with S-shaped transfer function

SBEWOA:

Enhanced SBWOA

SC:

Soft computing

SDLC:

Software sevelopment life cycle

SDP:

Software defect prediction

SFP:

Software fault prediction

SMOTE:

Synthetic minority oversampling technique

SSA:

Salp swarm algorithm

SVM:

Support vector machine

TF:

Transfer function

TN:

True negative

TNR:

True negative rate

TP:

True positive

TPR:

True positive rate

VBWOA:

Binary whale optimization algorithm with V-shaped transfer function

WOA:

Whale optimization algorithm

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Author information

Authors and Affiliations

  1. Department of Computer Science, Birzeit University, Birzeit, Palestine

    Majdi Mafarja

  2. Department of Computer Systems Engineering, Arab American University, Jenin, Palestine

    Thaer Thaher

  3. Information Technology Engineering, Al-Quds University, Abu Dies, Jerusalem, Palestine

    Thaer Thaher

  4. Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, United Arab EmiratesDeepSinghML2017, Irbid, Jordan

    Mohammed Azmi Al-Betar

  5. Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100, Durian Tunggal, Melaka, Malaysia

    Jingwei Too

  6. Department of Computer Science, Al-Aqsa University, P.O. Box 4051, Gaza, Palestine

    Mohammed A. Awadallah

  7. Artificial Intelligence Research Center (AIRC), Ajman University, Ajman, United Arab Emirates

    Mohammed A. Awadallah

  8. Department of Computing, College of Engineering and Applied Sciences, American University of Kuwait, Salmiya, Kuwait

    Iyad Abu Doush

  9. Computer Science Department, Yarmouk University, Irbid, Jordan

    Iyad Abu Doush

  10. Department of Health Management and Informatics, University of Missouri, Columbia, 5 Hospital Drive, Columbia, MO, 65212, USA

    Hamza Turabieh

Authors
  1. Majdi Mafarja
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  2. Thaer Thaher
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  3. Mohammed Azmi Al-Betar
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  4. Jingwei Too
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  5. Mohammed A. Awadallah
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  6. Iyad Abu Doush
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  7. Hamza Turabieh
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Corresponding author

Correspondence to Mohammed Azmi Al-Betar.

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Mafarja, M., Thaher, T., Al-Betar, M.A. et al. Classification framework for faulty-software using enhanced exploratory whale optimizer-based feature selection scheme and random forest ensemble learning. Appl Intell 53, 18715–18757 (2023). https://doi.org/10.1007/s10489-022-04427-x

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