A Method for Automatic Code Error Detection and Repair Based on Deep Learning

Deep learning (DL) frameworks possess robust feature extraction and pattern recognition abilities, enabling them to autonomously grasp and pinpoint error and vulnerability patterns within codebases. This article leverages a Deep Belief Network (DBN) framework to train and garner insights from extensive open-source code repositories, aiming to unearth and comprehend error and vulnerability patterns inherent in the code. Subsequently, these patterns are employed to scrutinize fresh codebases, pinpoint errors and vulnerabilities, and facilitate their rectification. In a comparative analysis with the Support Vector Machine (SVM) algorithm, our approach demonstrates a notable reduction in Mean Absolute Error (MAE) by 31.66% and an elevation in recall by 17.69% in the realm of software vulnerability detection. This underscores the superior accuracy and reliability of our method in detecting software vulnerabilities. The DBN excels in the automated detection and remediation of code errors and vulnerabilities, owing to its proficiency in extracting pertinent features from code and pinpointing errors and vulnerabilities with greater precision.