  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.
