Software Defect Prediction With Quantum‐Inspired Feature Selection

Accurate software defect prediction is crucial for reducing maintenance costs and improving system reliability. Effective feature selection ensures that models focus on the most informative metrics while avoiding overfitting and excessive complexity. We introduce a quantum‐inspired evolutionary algorithm (QIEA) that encodes candidate feature subsets using probability amplitudes and then iteratively refines them through adaptive rotation operations inspired by quantum mechanics. Our approach simultaneously optimizes prediction quality and model simplicity by balancing F1‐score maximization with subset size reduction. We evaluate QIEA on four public benchmarks—NASA PC1, CM1, and PROMISE KC1, JM1—using both within‐project cross‐validation and leave‐one‐project‐out protocols. Across all scenarios, QIEA delivers a consistent absolute improvement of six percentage points in F1‐score over classical genetic algorithms, while reducing the number of selected features by 30–40 percent. Hypervolume analysis confirms that our method achieves superior tradeoffs between accuracy and compactness. The experimental setup and parameter settings are described in detail throughout this paper.