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

• URL: http://arxiv.org/abs/2402.07081v1
• Date: Sun, 11 Feb 2024 01:37:48 GMT
• Title: Using Large Language Models for Student-Code Guided Test Case Generation in Computer Science Education
• Authors: Nischal Ashok Kumar, Andrew Lan
• Abstract summary: Test cases are an integral part of programming assignments in computer science education. Test cases can be used as assessment items to test students' programming knowledge and provide personalized feedback on student-written code. We propose a large language model-based approach to automatically generate test cases.
• Score: 2.5382095320488665
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Genetic Auto-prompt Learning for Pre-trained Code Intelligence Language Models [54.58108387797138]
  We investigate the effectiveness of prompt learning in code intelligence tasks. Existing automatic prompt design methods are very limited to code intelligence tasks. We propose Genetic Auto Prompt (GenAP) which utilizes an elaborate genetic algorithm to automatically design prompts.
  arXiv  Detail & Related papers  (2024-03-20T13:37:00Z)
• Identifying Student Profiles Within Online Judge Systems Using Explainable Artificial Intelligence [6.638206014723678]
  Online Judge (OJ) systems are typically considered within programming-related courses as they yield fast and objective assessments of the code developed by the students. This work aims to tackle this limitation by considering the further exploitation of the information gathered by the OJ and automatically inferring feedback for both the student and the instructor.
  arXiv  Detail & Related papers  (2024-01-29T12:11:30Z)
• Next-Step Hint Generation for Introductory Programming Using Large Language Models [0.8002196839441036]
  Large Language Models possess skills such as answering questions, writing essays or solving programming exercises. This work explores how LLMs can contribute to programming education by supporting students with automated next-step hints.
  arXiv  Detail & Related papers  (2023-12-03T17:51:07Z)
• Exploring the Potential of Large Language Models to Generate Formative Programming Feedback [0.5371337604556311]
  We explore the potential of large language models (LLMs) for computing educators and learners. To achieve these goals, we used students' programming sequences from a dataset gathered within a CS1 course as input for ChatGPT. Results show that ChatGPT performs reasonably well for some of the introductory programming tasks and student errors. However, educators should provide guidance on how to use the provided feedback, as it can contain misleading information for novices.
  arXiv  Detail & Related papers  (2023-08-31T15:22:11Z)
• Towards Informed Design and Validation Assistance in Computer Games Using Imitation Learning [65.12226891589592]
  This paper proposes a new approach to automated game validation and testing. Our method leverages a data-driven imitation learning technique, which requires little effort and time and no knowledge of machine learning or programming.
  arXiv  Detail & Related papers  (2022-08-15T11:08:44Z)
• What should I Ask: A Knowledge-driven Approach for Follow-up Questions Generation in Conversational Surveys [63.51903260461746]
  We propose a novel task for knowledge-driven follow-up question generation in conversational surveys. We constructed a new human-annotated dataset of human-written follow-up questions with dialogue history and labeled knowledge. We then propose a two-staged knowledge-driven model for the task, which generates informative and coherent follow-up questions.
  arXiv  Detail & Related papers  (2022-05-23T00:57:33Z)
• Data-Driven Approach for Log Instruction Quality Assessment [59.04636530383049]
  There are no widely adopted guidelines on how to write log instructions with good quality properties. We identify two quality properties: 1) correct log level assignment assessing the correctness of the log level, and 2) sufficient linguistic structure assessing the minimal richness of the static text necessary for verbose event description. Our approach correctly assesses log level assignments with an accuracy of 0.88, and the sufficient linguistic structure with an F1 score of 0.99, outperforming the baselines.
  arXiv  Detail & Related papers  (2022-04-06T07:02:23Z)
• ProtoTransformer: A Meta-Learning Approach to Providing Student Feedback [54.142719510638614]
  In this paper, we frame the problem of providing feedback as few-shot classification. A meta-learner adapts to give feedback to student code on a new programming question from just a few examples by instructors. Our approach was successfully deployed to deliver feedback to 16,000 student exam-solutions in a programming course offered by a tier 1 university.
  arXiv  Detail & Related papers  (2021-07-23T22:41:28Z)
• Let's Ask Students About Their Programs, Automatically [0.0]
  Students sometimes produce code that works but that its author does not comprehend. One way to tackle these issues is to probe students' comprehension by asking them questions about their own programs. We propose an approach to automatically generate questions about student-written program code.
  arXiv  Detail & Related papers  (2021-03-20T09:15:37Z)
• How do students test software units? [3.6748639131154315]
  We asked students to fill in a small survey, to do four exercises and to fill a second survey. We interviewed eleven students in semi-structured interviews, to obtain more in-depth insight. One of the misconceptions we found is that most students can only think of test cases based on programming code. Even if no code was provided (black-box testing), students try to come up with code to base their test cases on.
  arXiv  Detail & Related papers  (2021-02-16T07:02:59Z)
• Retrieve, Program, Repeat: Complex Knowledge Base Question Answering via Alternate Meta-learning [56.771557756836906]
  We present a novel method that automatically learns a retrieval model alternately with the programmer from weak supervision. Our system leads to state-of-the-art performance on a large-scale task for complex question answering over knowledge bases.
  arXiv  Detail & Related papers  (2020-10-29T18:28:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.