The recent progress in generative AI techniques has significantly influenced software engineering, as AI-driven methods tackle common developer challenges such as code synthesis from descriptions, program repair, and natural language summaries for existing programs. Large-scale language models (LLMs), like OpenAI's Codex, are increasingly adopted in AI-driven software engineering. ChatGPT, another LLM, has gained considerable attention for its potential as a bot for discussing source code, suggesting changes, providing descriptions, and generating code. To evaluate the practicality of LLMs as programming assistant bots, it is essential to examine their performance on unseen problems and various tasks. In our paper, we conduct an empirical analysis of ChatGPT's potential as a fully automated programming assistant, emphasizing code generation, program repair, and code summarization. Our study assesses ChatGPT's performance on common programming problems and compares it to state-of-the-art approaches using two benchmarks. Our research indicates that ChatGPT effectively handles typical programming challenges. However, we also discover the limitations in its attention span: comprehensive descriptions can restrict ChatGPT's focus and impede its ability to utilize its extensive knowledge for problem-solving. Surprisingly, we find that ChatGPT's summary explanations of incorrect code provide valuable insights into the developer's original intentions. This insight can be served as a foundation for future work addressing the oracle problem. Our study offers valuable perspectives on the development of LLMs for programming assistance, specifically by highlighting the significance of prompt engineering and enhancing our comprehension of ChatGPT's practical applications in software engineering.

我们通过实证分析了ChatGPT在无人辅助编程助手方面的潜力，并强调了其相对程序生成、程序修复、代码摘要方面的表现，并对其在常见编程问题上的表现进行了评估，这证明ChatGPT有效地处理典型的编程挑战，但我们也发现，综合的描述可能会限制ChatGPT的关注点并阻碍其利用其广泛的知识进行问题解决。

ChatGPT: 最终的编程助手吗？—— 它有多远？