  Large language models (LLMs) have demonstrated notable proficiency in code
generation, with numerous prior studies showing their promising capabilities in
various development scenarios. However, these studies mainly provide
evaluations in research settings, which leaves a significant gap in
understanding how effectively LLMs can support developers in real-world. To
address this, we conducted an empirical analysis of conversations in DevGPT, a
dataset collected from developers' conversations with ChatGPT (captured with
the Share Link feature on platforms such as GitHub). Our empirical findings
indicate that the current practice of using LLM-generated code is typically
limited to either demonstrating high-level concepts or providing examples in
documentation, rather than to be used as production-ready code. These findings
indicate that there is much future work needed to improve LLMs in code
generation before they can be integral parts of modern software development.
