Research on Software Defect Prediction Model based on Deep Learning

Abstract

As software systems grow in complexity and scale, detecting and predicting defects has become crucial for ensuring software quality and enhancing development efficiency. Traditional approaches to software defect prediction rely heavily on manual feature extraction and statistical models, which often struggle to handle intricate defect patterns and large-scale datasets. Recently, deep learning has demonstrated significant promise in software defect prediction, primarily due to its ability to automatically extract features and its strong pattern recognition capabilities. To enhance both the accuracy of defect predictions and the interpretability of the models, this paper proposes a deep learning-based prediction model. The focus is placed on the stages of data preprocessing, model selection, parameter tuning, and model assessment. The experimental outcomes indicate that the deep learning approach consistently outperforms conventional methods across various public datasets, achieving superior predictive accuracy and robustness. This study offers theoretical insights and practical evidence for further advancing the effectiveness of software defect prediction techniques.