Learning to Assess Software Quality: A Metric-Guided Peer Code Review Tool for Large-Scale Software

Evaluating the Effects of Automated Test Results, Model Solutions, and Checklists on Peer Code Review: A Quantitative Study

Comparative Evaluation of Large Language Models in Computer Programming Education

A comparative analysis of six LLMs for generating formative feedback on introductory Java programs containing predefined defects under controlled conditions reveals substantial cross-model variation, particularly in multi-defect scenarios.

Beyond Code Correctness: A Hybrid Behavioral-Cognitive Model for Student Assessment in Programming Education

 Traditional programming assignment assessments primarily focus on evaluating the correctness of the final output, often neglecting critical aspects such as engagement, collaboration, and debugging skills. As programming education increasingly shifts toward project-based and collaborative learning, there is a growing need for more holistic evaluation methods. This paper proposes a hybrid conceptual framework that integrates cognitive correctness metrics with behavioral analytics derived from Source Code Management (SCM) systems, such as GitHub. The framework captures authentic student behaviors during assignment development and leverages machine-learning models combined with explainable AI techniques to transparently predict grades. This conceptual study advocates a paradigm shift from purely product-based assessments to a more process-oriented, learning-centered evaluation model that promotes deeper and more meaningful educational experiences in programming education.

CodeStylist: Supporting Early Undergraduate Programmers with Course-Aware Code Style Feedback

Findings are interpreted as evidence that course-aware style feedback is promising as a pre-submission revision aid, but that future versions should combine deterministic rule checks with LLM-generated explanations, rule citations, and stronger verification support.

Peer Code Review in Research Software: Practices, Improvements, and Implementation Challenges from Interviews of RSEs

The results show that RSEs most often improved code review by using pull requests, defining review expectations, adding lightweight process steps, and using tools and automation to reduce effort and increase consistency.

From Repositories to Practice: LLM-Based Personalization in Programming Education