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

Raja Oueslati, Ghaith Manita

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 trad