Grading coding assignments manually is challenging due to complexity and
subjectivity. However, auto-grading with deep learning simplifies the task. It
objectively assesses code quality, detects errors, and assigns marks
accurately, reducing the burden on instructors while ensuring efficient and
fair assessment. This study provides an analysis of auto-grading of the C
programming assignments using machine learning and deep learning approaches
like regression, convolutional neural networks (CNN) and long short-term memory
(LSTM). Using a code-based transformer word embedding model called CodeBERT,
the textual code inputs were transformed into vectors, and the vectors were
then fed into several models. The testing findings demonstrated the efficacy of
the suggested strategy with a root mean squared error (RMSE) of 1.89. The
contrast between statistical methods and deep learning techniques is discussed
in the study.

利用深度学习的自动评分系统通过将程序文本输入转换为向量并利用多个模型进行分析，成功评估了 C 编程作业，取得了 1.89 的均方根误差。