Large language models (LLMs) are rapidly changing the tech landscape, promising to automate complex tasks like writing code and even fixing bugs. But how good are they really at tackling real-world software issues? A new research paper introduces FAUN-Eval, a benchmark designed to put LLMs to the test in a more realistic, granular way than ever before. Instead of just asking LLMs to generate code from scratch, FAUN-Eval challenges them with three key tasks that mirror the actual software development process: answering code-related questions (like a developer responding to a user report), pinpointing the buggy file within a large codebase, and finally, generating the correct code fix. The researchers tested a range of popular LLMs, both open-source and proprietary—from giants like GPT-4 to newer models like DeepSeek Coder and Gemini. The results? A mixed bag. While some LLMs excelled at certain tasks, no single model triumphed across the board. Interestingly, bigger wasn’t always better. Smaller, open-source models sometimes outperformed their larger, proprietary counterparts, suggesting that sheer size isn't the only factor in problem-solving prowess. The research also highlights some critical weaknesses. LLMs still struggle with real-world question answering, often missing the nuance of human communication. Even more concerning, some models completely ignored the researchers' instructions, revealing a worrying lack of reliability. Perhaps the most surprising finding was that certain elements of bug reports, like the title, could actually mislead LLMs. This suggests that the way we report bugs might need to change to make it easier for AI to understand. FAUN-Eval reveals a crucial truth: while LLMs show immense potential for automating bug fixes, they're not a silver bullet. The benchmark provides valuable insights into where LLMs shine and where they fall short, paving the way for more targeted research and development. The next step? Building LLMs that not only generate code but truly understand the complexities of software and the nuances of human communication.