Summary of Not the Silver Bullet: Llm-enhanced Programming Error Messages Are Ineffective in Practice, by Eddie Antonio Santos and Brett A. Becker
Not the Silver Bullet: LLM-enhanced Programming Error Messages are Ineffective in Practice
by Eddie Antonio Santos, Brett A. Becker
First submitted to arxiv on: 27 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The sudden rise of large language models (LLMs) like ChatGPT has significantly impacted the computing education community. Research shows that LLMs excel at generating correct code for CS1 and CS2 problems, and can even serve as friendly assistants for coding learners. Additionally, studies demonstrate that LLMs produce superior results in explaining and resolving compiler error messages, a decades-long challenge for programmers. However, these findings are based on expert assessments in artificial conditions. This study aimed to understand how novice programmers resolve programming error messages (PEMs) in a more realistic scenario. A within-subjects study with 106 participants was conducted, where students were tasked with fixing six buggy C programs using either stock compiler error messages, handwritten expert explanations, or GPT-4 generated error message explanations. Despite promising results on synthetic benchmarks, the study found that GPT-4 generated error messages outperformed conventional compiler error messages in only one task, measured by students’ time-to-fix each problem. Handwritten explanations still outperform LLM and conventional error messages, both objectively and subjectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how computers help people learn to code. It looks at special machines called Large Language Models (LLMs) that can explain tricky code problems. The authors wanted to see if these machines really help beginner programmers fix mistakes in their code. They did an experiment with 106 students who had to fix six faulty programs using different types of error messages. The results showed that the LLMs didn’t always do better than traditional methods, but handwritten explanations from experts still worked best. |
Keywords
» Artificial intelligence » Gpt
