MS-FSOA-LightGBM: Multi-Strategy Starfish Optimization Algorithm with LightGBM for Software Defect Prediction

Software Defect Prediction (SDP) aims to detect bugs at an early stage of the software development process, helping to improve quality while reducing costs and development time. Machine learning (ML) models, such as LightGBM, have shown strong performance, their effectiveness depends heavily on proper hyperparameter optimization. This paper introduces MS-FSOA, a multi-strategy enhancement of the Starfsh Optimization Algorithm, to optimize LightGBM for SDP. MS-FSOA integrates enhanced population initialization, Lévy flight, and adaptable cooperative hunting to improve search quality and maintain diversity throughout the optimization process. Experiments on three PROMISE datasets show that MS-FSOA significantly improves prediction performance. Compared to traditional algorithms, it boosts LightGBM’s accuracy and robustness in software defect classification.