Using Large Language Models for Student-Code Guided Test Case Generation in Computer Science Education

Article Status
Published
Authors/contributors
Title
Using Large Language Models for Student-Code Guided Test Case Generation in Computer Science Education
Abstract
In computer science education, test cases are an integral part of programming assignments since they can be used as assessment items to test students' programming knowledge and provide personalized feedback on student-written code. The goal of our work is to propose a fully automated approach for test case generation that can accurately measure student knowledge, which is important for two reasons. First, manually constructing test cases requires expert knowledge and is a labor-intensive process. Second, developing test cases for students, especially those who are novice programmers, is significantly different from those oriented toward professional-level software developers. Therefore, we need an automated process for test case generation to assess student knowledge and provide feedback. In this work, we propose a large language model-based approach to automatically generate test cases and show that they are good measures of student knowledge, using a publicly available dataset that contains student-written Java code. We also discuss future research directions centered on using test cases to help students.
Archive ID
arXiv:2402.07081
Date
2024-02-10
Citation Key
kumar2024
Accessed
26/02/2024, 18:02
Library Catalogue
Extra
arXiv:2402.07081 [cs] <标题>: 在计算机科学教育中使用大型语言模型生成学生代码指导的测试用例 <AI Smry>: A large language model-based approach is proposed to automatically generate test cases and show that they are good measures of student knowledge, using a publicly available dataset that contains student-written Java code.
Citation
Kumar, N. A., & Lan, A. (2024). Using Large Language Models for Student-Code Guided Test Case Generation in Computer Science Education (arXiv:2402.07081). https://doi.org/10.48550/arXiv.2402.07081
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