A Comparative Study of CNN and MLP for Software Defect Prediction Using PROMISE Datasets

Abstract

Modern Software defects pose significant challenges, leading to critical system failures and substantial financial losses. As contemporary software systems become increasingly large and complex, identifying defects during the early stages grows more difficult. To address this, deep learning techniques, specifically multi-layer perceptron (MLP) and Convolutional Neural Network (CNN), are employed to predict software defects (SDP), integrated with code segment analysis during early development phases. This study applies both algorithms to detect software defects using 11 open-source datasets from the PROMISE repository. The evaluation emphasizes the prediction accuracy of the MLP and CNN models, alongside the F1 score-a crucial metric for assessing model performance on imbalanced datasets. Findings indicate that CNN outperforms MLP, achieving 86% prediction accuracy and an F1 score of 87.7%. In contrast, MLP attained 71% accuracy with an F1 score of 71.6%. These results demonstrate the superior predictive capability of CNN-based approaches in software defect prediction tasks.