# Using Large Language Models to Enhance Programming Error Messages

arXiv: https://arxiv.org/abs/2210.11630 · HTML: https://mlsanity.com/paper/2210.11630/

## Key facts

- Cited by 67 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 46 references that are in the map.
- First posted on arXiv 20 Oct 2022.

## Builds on (most cited first, 4 of 4)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributions](https://mlsanity.com/paper/2108.09293/) (2021)
- [Automatic Generation of Programming Exercises and Code Explanations using Large Language Models](https://mlsanity.com/paper/2206.11861/) (2022)

## Built on by (most cited first, 30 of 67)

- [Programming Is Hard -- Or at Least It Used to Be: Educational Opportunities And Challenges of AI Code Generation](https://mlsanity.com/paper/2212.01020/) (2022)
- [The Robots are Here: Navigating the Generative AI Revolution in Computing Education](https://mlsanity.com/paper/2310.00658/) (2023)
- [CodeAid: Evaluating a Classroom Deployment of an LLM-based Programming Assistant that Balances Student and Educator Needs](https://mlsanity.com/paper/2401.11314/) (2024)
- ["It's Weird That it Knows What I Want": Usability and Interactions with Copilot for Novice Programmers](https://mlsanity.com/paper/2304.02491/) (2023)
- [Comparing Code Explanations Created by Students and Large Language Models](https://mlsanity.com/paper/2304.03938/) (2023)
- [Computing Education in the Era of Generative AI](https://mlsanity.com/paper/2306.02608/) (2023)
- [The Widening Gap: The Benefits and Harms of Generative AI for Novice Programmers](https://mlsanity.com/paper/2405.17739/) (2024)
- [Exploring the Responses of Large Language Models to Beginner Programmers' Help Requests](https://mlsanity.com/paper/2306.05715/) (2023)
- [Thrilled by Your Progress! Large Language Models (GPT-4) No Longer Struggle to Pass Assessments in Higher Education Programming Courses](https://mlsanity.com/paper/2306.10073/) (2023)
- [Generative AI for Programming Education: Benchmarking ChatGPT, GPT-4, and Human Tutors](https://mlsanity.com/paper/2306.17156/) (2023)
- [Generating High-Precision Feedback for Programming Syntax Errors using Large Language Models](https://mlsanity.com/paper/2302.04662/) (2023)
- [StudentEval: A Benchmark of Student-Written Prompts for Large Language Models of Code](https://mlsanity.com/paper/2306.04556/) (2023)
- [Automating Human Tutor-Style Programming Feedback: Leveraging GPT-4 Tutor Model for Hint Generation and GPT-3.5 Student Model for Hint Validation](https://mlsanity.com/paper/2310.03780/) (2023)
- [Exploring the Potential of Large Language Models to Generate Formative Programming Feedback](https://mlsanity.com/paper/2309.00029/) (2023)
- [Next-Step Hint Generation for Introductory Programming Using Large Language Models](https://mlsanity.com/paper/2312.10055/) (2023)
- [Open Source Language Models Can Provide Feedback: Evaluating LLMs' Ability to Help Students Using GPT-4-As-A-Judge](https://mlsanity.com/paper/2405.05253/) (2024)
- [Promptly: Using Prompt Problems to Teach Learners How to Effectively Utilize AI Code Generators](https://mlsanity.com/paper/2307.16364/) (2023)
- [Can Language Models Employ the Socratic Method? Experiments with Code Debugging](https://mlsanity.com/paper/2310.03210/) (2023)
- [Feedback-Generation for Programming Exercises With GPT-4](https://mlsanity.com/paper/2403.04449/) (2024)
- [Large Language Model-Enabled Multi-Agent Manufacturing Systems](https://mlsanity.com/paper/2406.01893/) (2024)
- [Beyond the Hype: A Comprehensive Review of Current Trends in Generative AI Research, Teaching Practices, and Tools](https://mlsanity.com/paper/2412.14732/) (2024)
- [ChatGPT in the Classroom: An Analysis of Its Strengths and Weaknesses for Solving Undergraduate Computer Science Questions](https://mlsanity.com/paper/2304.14993/) (2023)
- [Neural Task Synthesis for Visual Programming](https://mlsanity.com/paper/2305.18342/) (2023)
- [Evaluating ChatGPT and GPT-4 for Visual Programming](https://mlsanity.com/paper/2308.02522/) (2023)
- [Evaluating Contextually Personalized Programming Exercises Created with Generative AI](https://mlsanity.com/paper/2407.11994/) (2024)
- [Dcc --help: Generating Context-Aware Compiler Error Explanations with Large Language Models](https://mlsanity.com/paper/2308.11873/) (2023)
- [How Novice Programmers Use and Experience ChatGPT when Solving Programming Exercises in an Introductory Course](https://mlsanity.com/paper/2407.20792/) (2024)
- ["It's not like Jarvis, but it's pretty close!" -- Examining ChatGPT's Usage among Undergraduate Students in Computer Science](https://mlsanity.com/paper/2311.09651/) (2023)
- [Generative AI in Education: A Study of Educators' Awareness, Sentiments, and Influencing Factors](https://mlsanity.com/paper/2403.15586/) (2024)
- [AI-enhanced Auto-correction of Programming Exercises: How Effective is GPT-3.5?](https://mlsanity.com/paper/2311.10737/) (2023)

## Abstract (arXiv)

A key part of learning to program is learning to understand programming error messages. They can be hard to interpret and identifying the cause of errors can be time-consuming. One factor in this challenge is that the messages are typically intended for an audience that already knows how to program, or even for programming environments that then use the information to highlight areas in code. Researchers have been working on making these errors more novice friendly since the 1960s, however progress has been slow. The present work contributes to this stream of research by using large language models to enhance programming error messages with explanations of the errors and suggestions on how to fix the error. Large language models can be used to create useful and novice-friendly enhancements to programming error messages that sometimes surpass the original programming error messages in interpretability and actionability. These results provide further evidence of the benefits of large language models for computing educators, highlighting their use in areas known to be challenging for students. We further discuss the benefits and downsides of large language models and highlight future streams of research for enhancing programming error messages.