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Compiler error messages serve as an initial resource for programmers dealingwith compilation errors. However, previous studies indicate that they oftenlack 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 recentadvances in large language models offer alternatives. This study systematicallyexamines 100 compiler error messages from three sources to determine the mosteffective approach for programmers encountering compiler errors. Factorsconsidered include Stack Overflow search methods and the impact of modelversion and prompt phrasing when using large language models. The resultsreveal that GPT-4 outperforms Stack Overflow in explaining compiler errormessages, the effectiveness of adding code snippets to Stack Overflow searchesdepends on the search method, and results for Stack Overflow differsignificantly between Google and StackExchange API searches. Furthermore, GPT-4surpasses GPT-3.5, with "How to fix" prompts yielding superior outcomes to"What does this error mean" prompts. These results offer valuable guidance forprogrammers seeking assistance with compiler error messages, underscoring thetransformative potential of advanced large language models like GPT-4 indebugging and opening new avenues of exploration for researchers in AI-assistedprogramming.

Addressing Compiler Errors: Stack Overflow or Large Language Models?