A hybrid detection framework that merges CodeBERT semantic representations with an XGBoost-based feature fusion model is suggested that achieves a recall rate of 75% and significantly outperforms general baseline methods in both accuracy and consistency.
Large language models can generate plausible solutions to programming assignments, making it tempting to detect their use by matching student code against a reference bank of generated solutions. Yet similar code can also arise when an assignment admits only a few natural implementations, which leaves open what a match...
Runlong Ye, Jing Fan, Angela Zavaleta Bernuy et al.· 0 citations
An updated evaluation of the capabilities and limitations of contemporary GenAI systems on authentic introductory OOP assessments is provided and evidence that can inform the design of programming assessments, the responsible integration of GenAI tools into software engineering education, and future studies evaluating...
In recent years, the importance of mathematical, data science, and AI education has grown significantly, making it necessary to learn machine learning programming. In programming education, while acquiring knowledge through lectures is important, the experience of actually creating programs helps solidify that knowledg...
A. Wakatani, T. Maeda· Vol. 12 No. 01 (2026): Proce...· 0 citations
Two novel contributions are introduced: CodeEval and CodeQual, an open-source execution framework that provides researchers with a ready-to-use evaluation pipeline for evaluating and improving LLMs in software engineering contexts, encompassing both functional correctness assessment and subjective code quality evaluati...
Fundamental concepts of generative artificial intelligence, the importance of prompt engineering, prompting techniques and strategies for improving the quality of responses generated by large language models, and the potential applications of these tools in programming education are presented.
Branislava Radu, Ljubica Kazi· 16th International Symposium...· 0 citations
LLMSuite is proposed, a hybrid test generation framework that integrates self-refinement prompting with class-level LLM reasoning into the search-based testing process and complements manually written test suites by exercising domain-specific behaviors that are often left untested.
Amirhossein Deljouyi, Annibale Panichella, Andy Zaidman· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
