Not the Silver Bullet: LLM-enhanced Programming Error Messages are Ineffective in Practice

TL;DR

This study evaluates the effectiveness of LLM-generated programming error messages in real-world scenarios, finding they are generally less effective than expert explanations and traditional compiler messages for novice programmers.

Contribution

It provides empirical evidence on the limited practical effectiveness of LLM-generated error explanations compared to expert and compiler messages in novice programming tasks.

Findings

LLM-generated error messages outperform compiler messages in only 1 of 6 tasks.

Handwritten explanations outperform both LLM and compiler messages.

LLMs show promise but are not yet reliable for novice debugging assistance.

Abstract

The sudden emergence of large language models (LLMs) such as ChatGPT has had a disruptive impact throughout the computing education community. LLMs have been shown to excel at producing correct code to CS1 and CS2 problems, and can even act as friendly assistants to students learning how to code. Recent work shows that LLMs demonstrate unequivocally superior results in being able to explain and resolve compiler error messages -- for decades, one of the most frustrating parts of learning how to code. However, LLM-generated error message explanations have only been assessed by expert programmers in artificial conditions. This work sought to understand how novice programmers resolve programming error messages (PEMs) in a more realistic scenario. We ran a within-subjects study with $n$ = 106 participants in which students were tasked to fix six buggy C programs. For each program, participants were randomly assigned to fix the problem using either a stock compiler error message, an expert-handwritten error message, or an error message explanation generated by GPT-4. Despite promising evidence on synthetic benchmarks, we found that GPT-4 generated error messages outperformed conventional compiler error messages in only 1 of the 6 tasks, measured by students' time-to-fix each problem. Handwritten explanations still outperform LLM and conventional error messages, both on objective and subjective measures.