Mining patterns in syntax trees to automate code reviews of student solutions for programming exercises
This addresses the challenge of providing consistent and timely feedback for student programming exercises, though it is incremental as it builds on existing code analysis techniques.
The authors tackled the problem of labor-intensive manual feedback in programming education by introducing ECHO, a machine learning method that analyzes patterns in abstract syntax trees to automate feedback reuse in code reviews. Their results show that ECHO can accurately and quickly predict appropriate feedback annotations, significantly reducing time and effort in educational settings.
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.