In programming education, providing manual feedback is essential but
labour-intensive, posing challenges in consistency and timeliness. We introduce
ECHO, a machine learning method to automate the reuse of feedback in
educational code reviews by analysing patterns in abstract syntax trees. This
study investigates two primary questions: whether ECHO can predict feedback
annotations to specific lines of student code based on previously added
annotations by human reviewers (RQ1), and whether its training and prediction
speeds are suitable for using ECHO for real-time feedback during live code
reviews by human reviewers (RQ2). Our results, based on annotations from both
automated linting tools and human reviewers, show that ECHO can accurately and
quickly predict appropriate feedback annotations. Its efficiency in processing
and its flexibility in adapting to feedback patterns can significantly reduce
the time and effort required for manual feedback provisioning in educational
settings.

使用机器学习方法 ECHO，通过分析抽象语法树中的模式，自动重复使用教育代码审查中的反馈，以解决编程教育中提供人工反馈的挑战。研究结果表明，ECHO 能够准确且快速地预测适当的反馈注释，大大减少了教育环境中提供人工反馈所需的时间和工作量。

在语法树中挖掘模式以自动化学生编程练习代码审查

Mining patterns in syntax trees to automate code reviews of student  solutions for programming exercises

ChatGPT, as a versatile large language model, has demonstrated remarkable
potential in addressing inquiries across various domains. Its ability to
analyze, comprehend, and synthesize information from both online sources and
user inputs has garnered significant attention. Previous research has explored
ChatGPT's competence in code generation and code reviews. In this paper, we
delve into ChatGPT's capabilities in security-oriented program analysis,
focusing on perspectives from both attackers and security analysts. We present
a case study involving several security-oriented program analysis tasks while
deliberately introducing challenges to assess ChatGPT's responses. Through an
examination of the quality of answers provided by ChatGPT, we gain a clearer
understanding of its strengths and limitations in the realm of
security-oriented program analysis.

ChatGPT 对安全导向的程序分析的能力进行了研究，从攻击者和安全分析师的角度出发，通过引入挑战性任务评估 ChatGPT 的回答质量，以更清楚地了解其在安全导向的程序分析领域的优势和限制。