Addressing Compiler Errors: Stack Overflow or Large Language Models?

TL;DR

This study compares the effectiveness of Stack Overflow and large language models, especially GPT-4, in explaining and resolving compiler error messages, highlighting GPT-4's superior performance and influencing search strategies.

Contribution

It provides a systematic evaluation of GPT-4 versus Stack Overflow for compiler errors, revealing insights into search methods, prompt phrasing, and model performance improvements.

Findings

GPT-4 outperforms Stack Overflow in explaining errors

Adding code snippets improves Stack Overflow search effectiveness depending on method

GPT-4 surpasses GPT-3.5, with specific prompts yielding better results

Abstract

Compiler error messages serve as an initial resource for programmers dealing with compilation errors. However, previous studies indicate that they often lack sufficient targeted information to resolve code issues. Consequently, programmers typically rely on their own research to fix errors. Historically, Stack Overflow has been the primary resource for such information, but recent advances in large language models offer alternatives. This study systematically examines 100 compiler error messages from three sources to determine the most effective approach for programmers encountering compiler errors. Factors considered include Stack Overflow search methods and the impact of model version and prompt phrasing when using large language models. The results reveal that GPT-4 outperforms Stack Overflow in explaining compiler error messages, the effectiveness of adding code snippets to Stack Overflow searches depends on the search method, and results for Stack Overflow differ significantly between Google and StackExchange API searches. Furthermore, GPT-4 surpasses GPT-3.5, with "How to fix" prompts yielding superior outcomes to "What does this error mean" prompts. These results offer valuable guidance for programmers seeking assistance with compiler error messages, underscoring the transformative potential of advanced large language models like GPT-4 in debugging and opening new avenues of exploration for researchers in AI-assisted programming.