Enhancing Programming Error Messages in Real Time with Generative AI

• URL: http://arxiv.org/abs/2402.08072v1
• Date: Mon, 12 Feb 2024 21:32:05 GMT
• Title: Enhancing Programming Error Messages in Real Time with Generative AI
• Authors: Bailey Kimmel, Austin Geisert, Lily Yaro, Brendan Gipson, Taylor Hotchkiss, Sidney Osae-Asante, Hunter Vaught, Grant Wininger, Chase Yamaguchi
• Abstract summary: We implement feedback from ChatGPT for all programs submitted to our automated assessment tool, Athene. Our results indicate that adding generative AI to an automated assessment tool does not necessarily make it better.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generative AI is changing the way that many disciplines are taught, including computer science. Researchers have shown that generative AI tools are capable of solving programming problems, writing extensive blocks of code, and explaining complex code in simple terms. Particular promise has been shown in using generative AI to enhance programming error messages. Both students and instructors have complained for decades that these messages are often cryptic and difficult to understand. Yet recent work has shown that students make fewer repeated errors when enhanced via GPT-4. We extend this work by implementing feedback from ChatGPT for all programs submitted to our automated assessment tool, Athene, providing help for compiler, run-time, and logic errors. Our results indicate that adding generative AI to an automated assessment tool does not necessarily make it better and that design of the interface matters greatly to the usability of the feedback that GPT-4 provided.

Related papers

• Programming with AI: Evaluating ChatGPT, Gemini, AlphaCode, and GitHub Copilot for Programmers [0.0]
  This study presents a thorough evaluation of leading programming assistants, including ChatGPT, Gemini(Bard AI), AlphaCode, and GitHub Copilot. It emphasizes the need for ethical developmental practices to actualize AI models' full potential.
  arXiv  Detail & Related papers  (2024-11-14T06:40:55Z)
• Evaluating GPT-4 at Grading Handwritten Solutions in Math Exams [48.99818550820575]
  We leverage state-of-the-art multi-modal AI models, in particular GPT-4o, to automatically grade handwritten responses to college-level math exams. Using real student responses to questions in a probability theory exam, we evaluate GPT-4o's alignment with ground-truth scores from human graders using various prompting techniques.
  arXiv  Detail & Related papers  (2024-11-07T22:51:47Z)
• 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)
• Whodunit: Classifying Code as Human Authored or GPT-4 Generated -- A case study on CodeChef problems [0.13124513975412253]
  We use code stylometry and machine learning to distinguish between GPT-4 generated and human-authored code. Our dataset comprises human-authored solutions from CodeChef and AI-authored solutions generated by GPT-4. Our study shows that code stylometry is a promising approach for distinguishing between GPT-4 generated code and human-authored code.
  arXiv  Detail & Related papers  (2024-03-06T19:51:26Z)
• The Responsible Development of Automated Student Feedback with Generative AI [6.008616775722921]
  This paper identifies four critical ethical considerations for implementing generative AI tools to provide automated feedback to students. The goal of this work is to enable the use of AI systems to automate mundane assessment and feedback tasks, without introducing a "tyranny of the majority"
  arXiv  Detail & Related papers  (2023-08-29T14:29:57Z)
• FacTool: Factuality Detection in Generative AI -- A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarios [87.12753459582116]
  A wider range of tasks now face an increasing risk of containing factual errors when handled by generative models. We propose FacTool, a task and domain agnostic framework for detecting factual errors of texts generated by large language models.
  arXiv  Detail & Related papers  (2023-07-25T14:20:51Z)
• A LLM Assisted Exploitation of AI-Guardian [57.572998144258705]
  We evaluate the robustness of AI-Guardian, a recent defense to adversarial examples published at IEEE S&P 2023. We write none of the code to attack this model, and instead prompt GPT-4 to implement all attack algorithms following our instructions and guidance. This process was surprisingly effective and efficient, with the language model at times producing code from ambiguous instructions faster than the author of this paper could have done.
  arXiv  Detail & Related papers  (2023-07-20T17:33:25Z)
• A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need? [112.12974778019304]
  generative AI (AIGC, a.k.a AI-generated content) has made headlines everywhere because of its ability to analyze and create text, images, and beyond. In the era of AI transitioning from pure analysis to creation, it is worth noting that ChatGPT, with its most recent language model GPT-4, is just a tool out of numerous AIGC tasks. This work focuses on the technological development of various AIGC tasks based on their output type, including text, images, videos, 3D content, etc.
  arXiv  Detail & Related papers  (2023-03-21T10:09:47Z)
• How Generative AI models such as ChatGPT can be (Mis)Used in SPC Practice, Education, and Research? An Exploratory Study [2.0841728192954663]
  Generative Artificial Intelligence (AI) models have the potential to revolutionize Statistical Process Control (SPC) practice, learning, and research. These tools are in the early stages of development and can be easily misused or misunderstood. We explore ChatGPT's ability to provide code, explain basic concepts, and create knowledge related to SPC practice, learning, and research.
  arXiv  Detail & Related papers  (2023-02-17T15:48:37Z)
• Generation Probabilities Are Not Enough: Uncertainty Highlighting in AI Code Completions [54.55334589363247]
  We study whether conveying information about uncertainty enables programmers to more quickly and accurately produce code. We find that highlighting tokens with the highest predicted likelihood of being edited leads to faster task completion and more targeted edits.
  arXiv  Detail & Related papers  (2023-02-14T18:43:34Z)
• Smart tutor to provide feedback in programming courses [0.0]
  We present an AI based intelligent tutor that answers students programming questions. The tool has been tested by university students at the URJC along a whole course.
  arXiv  Detail & Related papers  (2023-01-24T11:00:06Z)

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.