Jul 2026· Annual Conference on Innovation and Technology in Computer Science Education· pp. 359-365· 0 citations· 54 references
Computer Science
TL;DR
EvOwl includes novel features that are designed with large-scale and long-term projects in mind, such as support for using code coverage to focus students' attention on specific features within a large code base and a metric-guided approach to help students compare software quality across different implementations.
Abstract
Software engineers must be able to create software that is not only correct, but of high quality. Consequently, computer science educators must emphasize software design principles and give students timely, accurate feedback on code quality. Prior work has shown that peer code review can effectively reinforce software design principles while providing timely feedback at scale; however, most existing tools have been designed primarily for introductory courses. This paper presents the design and evaluation of EvOwl, a peer code review tool tailored to upper-level software engineering courses. EvOwl includes novel features that are designed with large-scale and long-term projects in mind, such as support for using code coverage to focus students' attention on specific features within a large code base and a metric-guided approach to help students compare software quality across different implementations.
Qu quantitative analysis of 622 peer reviews on review time, length, and correctness reveals that students are better at assessing actual correct submissions as correct than incorrect ones as incorrect, with test results and checklists slightly increasing their performance in identifying incorrect submissions.
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.
Melina Najimi, Saba Yazdani, Marzieh Ahmadzadeh· Proceedings of the Canadian...· 0 citations
A hybrid conceptual framework that integrates cognitive correctness metrics with behavioral analytics derived from Source Code Management systems, such as GitHub is proposed, which captures authentic student behaviors during assignment development and leverages machine-learning models combined with explainable AI techn...
Amitkumar R. Patel, Jaykumar Patel, Sandipkumar Surati· International journal of com...· 0 citations
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.
Ethan Dickey, L. Vento, Peter Kurto et al.· 0 citations
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.
Md. Ariful Islam Malik, Jeffrey C. Carver, Nasir U. Eisty· Practice and Experience in A...· 0 citations
The design and implementation of a web application that connects to a university version control system, analyzes student-selected repositories, and generates programming challenges targeted at weaknesses identified in the submitted source code is presented.
M. Horváth, Michaela Durkovicová, Lenka Bubenková et al.· International Computer Progr...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.