  Compilation is an important process in developing configurable systems, such
as Linux. However, identifying compilation errors in configurable systems is
not straightforward because traditional compilers are not variability-aware.
Previous approaches that detect some of these compilation errors often rely on
advanced techniques that require significant effort from programmers. This
study evaluates the efficacy of Large Language Models (LLMs), specifically
ChatGPT4, Le Chat Mistral and Gemini Advanced 1.5, in identifying compilation
errors in configurable systems. Initially, we evaluate 50 small products in
C++, Java, and C languages, followed by 30 small configurable systems in C,
covering 17 different types of compilation errors. ChatGPT4 successfully
identified most compilation errors in individual products and in configurable
systems, while Le Chat Mistral and Gemini Advanced 1.5 detected some of them.
LLMs have shown potential in assisting developers in identifying compilation
errors in configurable systems.
