Using Large Language Models to Enhance Programming Error Messages

Juho Leinonen, Arto Hellas, Sami Sarsa, Brent Reeves, Paul Denny, James Prather, Brett A. Becker

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

65 Citations (Scopus)
120 Downloads (Pure)

Abstract

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 them. 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.

Original languageEnglish
Title of host publicationSIGCSE 2023 - Proceedings of the 54th ACM Technical Symposium on Computer Science Education
PublisherACM
Pages563–569
Number of pages7
ISBN (Print)978-1-4503-9431-4
DOIs
Publication statusPublished - 2 Mar 2023
MoE publication typeA4 Conference publication
EventACM Technical Symposium on Computer Science Education - Toronto, Canada
Duration: 15 Mar 202318 Mar 2023
Conference number: 54

Conference

ConferenceACM Technical Symposium on Computer Science Education
Abbreviated titleSIGCSE
Country/TerritoryCanada
CityToronto
Period15/03/202318/03/2023

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