##基于神经网络的程序错误类型识别

Abstract

This paper presents a novel approach to program error type identification utilizing deep neural networks. The core idea is to automate the process of classifying different types of errors found within program code. Traditional methods for error identification are often manual, time-consuming, and reliant on expert knowledge. This research proposes a system that leverages the pattern recognition capabilities of neural networks, trained on substantial datasets of code and corresponding error examples, to achieve automated error type classification. The system is designed to learn complex features indicative of various error types, offering a potentially more efficient and scalable solution compared to existing approaches. The presented architecture consists of a deep neural network, trained to map code snippets directly to their respective error categories. The effectiveness of the system is evaluated through a series of tests, demonstrating its ability to accurately identify a range of common programming errors. The system's potential applications span across software development, testing, and debugging, contributing to improved software quality and reduced development cycles.