Using Large Language Models for Student-Code Guided Test Case Generation
  in Computer Science Education
Nischal Ashok Kumar Andrew Lan
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.