Software Defect Prediction Using Integrated Logistic Regression and Fractional Chaotic Grey Wolf Optimizer

Raja Oueslati, Ghaith Manita, Ghaith Manita

2024

Abstract

Software Defect Prediction (SDP) is critical for enhancing the reliability and efficiency of software development processes. This study introduces a novel approach, integrating Logistic Regression (LR) with the Fractional Chaotic Grey Wolf Optimizer (FCGWO), to address the challenges in SDP. This integration’s primary objective is to overcome LR’s limitations, particularly in handling complex, high-dimensional datasets and mitigating overfitting. FCGWO, inspired by the social and hunting behaviours of grey wolves, coupled with the dynamism of Fractional Chaotic maps, offers an advanced optimization technique. It refines LR’s parameter tuning, enabling it to navigate intricate data landscapes more effectively. The methodology involved applying the LR-FCGWO model to various SDP datasets, focusing on optimizing the LR parameters for enhanced prediction accuracy. The results demonstrate a significant improvement in defect prediction performance, with the LR-FCGWO model outperforming traditional LR models in accuracy and robustness. The study concludes that integrating LR and FCGWO presents a promising advance in SDP, offering a more reliable, efficient, and accurate approach for predicting software defects.

Paper Citation

in Harvard Style

Oueslati R. and Manita G. (2024). Software Defect Prediction Using Integrated Logistic Regression and Fractional Chaotic Grey Wolf Optimizer. In Proceedings of the 19th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE; ISBN 978-989-758-696-5, SciTePress, pages 633-640. DOI: 10.5220/0012704600003687

in Bibtex Style

@conference{enase24,
author={Raja Oueslati and Ghaith Manita},
title={Software Defect Prediction Using Integrated Logistic Regression and Fractional Chaotic Grey Wolf Optimizer},
booktitle={Proceedings of the 19th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE},
year={2024},
pages={633-640},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012704600003687},
isbn={978-989-758-696-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE
TI - Software Defect Prediction Using Integrated Logistic Regression and Fractional Chaotic Grey Wolf Optimizer
SN - 978-989-758-696-5
AU - Oueslati R.
AU - Manita G.
PY - 2024
SP - 633
EP - 640
DO - 10.5220/0012704600003687
PB - SciTePress