Fix abstract and title in kumar24

_posts/2024-08-09-kumar24.md

@@ -1,16 +1,15 @@
 ---
-title: 'Using Large Language Models for Student-Code GuidedTest Case Generation in Computer Science Education'
+title: 'Using Large Language Models for Student-Code Guided Test Case Generation in Computer Science Education'
 booktitle: Proceedings of the 2024 AAAI Conference on Artificial Intelligence
 year: '2024'
-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 knowledgeand 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 testcases 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.'
+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 knowledgeand 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 testcases 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."
 layout: inproceedings
 series: Proceedings of Machine Learning Research
 publisher: PMLR
 issn: 2640-3498
 id: kumar24
 month: 0
-tex_title: 'Learning to Compare Hints: Combining Insights from Student Logs and Large
-  Language Models'
+tex_title: 'Using Large Language Models for Student-Code Guided Test Case Generation in Computer Science Education'
 firstpage: 170
 lastpage: 179
 page: 170-179