大模型，为何搞不定软件开发？根本原因就在…

Core Viewpoint - The article discusses the limitations of Large Language Models (LLMs) in software development, emphasizing that while LLMs can generate code and assist with simple tasks, they struggle with maintaining clear cognitive models necessary for complex problem-solving [5][14][15]. Group 1: LLM Capabilities - LLMs can perform routine engineering tasks such as reading code, writing tests, and debugging, but they often fail to maintain a coherent understanding of the code's behavior [8][15]. - They can generate code quickly and are effective in organizing requirement documents for straightforward tasks [15][16]. Group 2: Limitations of LLMs - LLMs cannot maintain two similar cognitive models simultaneously, which leads to confusion in determining whether to modify the code or the requirements [14][20]. - They often assume their generated code is flawless and struggle to adapt when tests fail, lacking the ability to validate their work against a clear mental model [9][14][22]. Group 3: Future Improvements - There is potential for improvement in LLMs, but significant changes to their underlying architecture are necessary to enhance their problem-solving capabilities beyond mere code generation [12][21]. - The article suggests that while LLMs currently have shortcomings, their rapid evolution indicates that they may become more competent in software development tasks in the future [21][22]. Group 4: Human vs. LLM Collaboration - The article advocates for human oversight in software development, asserting that LLMs should be viewed as tools rather than replacements for human engineers [17][19]. - It highlights the importance of human engineers in ensuring clarity in requirements and the actual effectiveness of the code produced [16][17].