Developing a Machine Learning-Based Software Fault Prediction Model Using the Improved Whale Optimization Algorithm

Abstract

Software fault prediction (SFP) is vital for ensuring software system reliability by detecting and mitigating faults. Machine learning has proven effective in addressing SFP challenges. However, extensive fault data from historical repositories often lead to dimensionality issues due to numerous metrics. Feature selection (FS) helps mitigate this problem by identifying key features. This research enhances the Whale Optimization Algorithm (WOA) by combining truncation selection with a single-point crossover method to enhance exploration and avoid local optima. Evaluating the enhancement on 14 SFP datasets from the PROMISE repository reveals its superiority over the original WOA and other variants, demonstrating its potential for improved SFP.

Keywords

• software fault prediction
• whale optimization
• feature selection
• machine learning
• truncation selection