Using Large Language Models to Enhance Programming Error Messages
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2022
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| _version_ | 1866911821931216896 |
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| author | Leinonen, Juho Hellas, Arto Sarsa, Sami Reeves, Brent Denny, Paul Prather, James Becker, Brett A. |
| author_facet | Leinonen, Juho Hellas, Arto Sarsa, Sami Reeves, Brent Denny, Paul Prather, James Becker, Brett A. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2210_11630 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Using Large Language Models to Enhance Programming Error Messages Leinonen, Juho Hellas, Arto Sarsa, Sami Reeves, Brent Denny, Paul Prather, James Becker, Brett A. Human-Computer Interaction Artificial Intelligence Computation and Language Software Engineering 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. |
| title | Using Large Language Models to Enhance Programming Error Messages |
| topic | Human-Computer Interaction Artificial Intelligence Computation and Language Software Engineering |
| url | https://arxiv.org/abs/2210.11630 |