Deep Learning Powered Software Bug Prediction using CNN and LSTM Hybrid Architecture
Keywords:
Code analysis, Deep learning, Intelligent bug detection, CNN–LSTM, Software Defect prediction, Software quality assuranceAbstract
The rapid growth of modern software applications has significantly increased the complexity of software systems. As projects become larger and more sophisticated, identifying defects in source code has become a major challenge for developers and software engineers. Early detection of software bugs is essential because it helps reduce development costs, improves system reliability, and ensures better software quality. Traditional defect prediction approaches mainly rely on manually designed code metrics and rule-based analysis techniques. Although these methods can identify certain types of errors, they often fail to capture deeper structural and contextual relationships within the code. To address these limitations, this study introduces DeepGuard AI, an intelligent defect prediction framework based on a hybrid deep learning architecture that combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. The proposed system is designed to automatically learn meaningful patterns from raw source code without relying solely on manually defined features. The framework includes multiple stages such as data preprocessing, tokenization, embedding, feature extraction, sequential modeling, classification, and result visualization. By integrating these components into a single pipeline, the system can support efficient and accurate bug prediction. Experimental results indicate that the proposed model achieves better predictive performance than several traditional machine learning approaches used in software defect prediction.
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