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An Efficient Hybrid Mine Blast Algorithm for Tackling Software Fault Prediction Problem

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Abstract

An inherent problem in software engineering is that competing prediction systems have been found to produce conflicting results. Yet accurate prediction is crucial because the complexity and quality of software requirements have dramatically changed in recent years, and consumers have become considerably more demanding in terms of the cost, timeframe, and quality of software solutions. Moreover, these variables may also be in direct conflict and can only be resolved by the optimum development of software by using reliable software engineering strategies. In this paper, a novel method based on the integration of the mine blast algorithm (MBA) and the simulated annealing (SA) algorithm is used to create input connection weights and biases for a back propagation neural network (BPNN) for the purpose of addressing the software fault prediction problem (SFP). The aim of hybridizing the MBA and SA is to find a way to efficiently explore and manipulate the search space. The proposed MBA-SA was tested on 18 datasets for SFP. The results indicated that the MBA-SA outperformed the MBA on all datasets. These results were subjected to additional statistical validity, boxplot distribution, and convergence analysis. Furthermore, a comparative evaluation of MBA-SA against twenty state-of-the-art methods for various output metrics was performed, and the result indicated that the hybrid MBA-SA outperformed most other state-of-the-art methods in the majority of datasets.

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Abbreviations

BPSO:

Binary particle swarm optimization

BSSA:

Binary spring search algorithm

DE:

Differential evolution

DT:

Decision tree

FA:

Firefly algorithm

GA:

Genetic algorithm

HSA:

Harmony search algorithm

J48:

Decision tree

KNN:

K-nearest neighbors algorithm

KS:

K-star

LB:

Lower bound

LDA:

Latent dirichlet allocation algorithm

LiOpFS:

Lion optimization based feature selection

LR:

Logistic regression

MBA:

Mine blast algorithm

ML:

Machine learning

NB:

Naïve bayes

OR:

OneR

RBF:

Radial basis function

PSO:

Particle swarm optimization

PG:

Pegasos

SA:

Simulated annealing algorithm

SEPP:

Software engineering prediction problem

SMO:

Sequential minimal optimization

SFP:

Software fault prediction problem

SVM:

Support vector machine algorithm

TS:

Tabu search

UB:

Upper bound

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Acknowledgements

The research reported in this publication was supported by the Deanship of Scientific Research at Al-Balqa Applied University in Jordan, Grant Number: DSR-2021#369.

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Authors and Affiliations

  1. Department of CS, Prince Abdullah bin Ghazi Faculty of Communication and Information Technology, Al-Balqa Applied University, Salt, Jordan

    Mohammed Alweshah

  2. Artificial Intelligence Department, Faculty of Information Technology, Aqaba University of Technology, Aqaba, Jordan

    Mohammed Alweshah & Ibrahim Altarawni

  3. Software Engineering Department, Faculty of Information Technology, Aqaba University of Technology, Aqaba, Jordan

    Sofian Kassaymeh

  4. CAIT, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Malaysia

    Saleh Alkhalaileh

  5. Computer Science Department, Tafila Technical University, Tafila, Jordan

    Mohammad Almseidin

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  1. Mohammed Alweshah
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Alweshah, M., Kassaymeh, S., Alkhalaileh, S. et al. An Efficient Hybrid Mine Blast Algorithm for Tackling Software Fault Prediction Problem. Neural Process Lett 55, 10925–10950 (2023). https://doi.org/10.1007/s11063-023-11357-3

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