别被骗了，AI Coding可没那么神，22名软件开发者道出了这些弊端

Core Insights - The rapid advancement of software output speed is significantly influenced by large language models (LLMs) like ChatGPT and GitHub Copilot, which are reshaping the way software developers work [1][2] - While LLMs have increased developer efficiency by 26%, they raise questions about the essence of software development and the potential dilution of creativity and critical thinking [1][2] Research Findings - LLMs enhance developer productivity, maintain development processes, and promote entrepreneurship, but they also pose risks such as damaging developer reputation, fostering laziness, and hindering skill development [2][11] - The research utilized a social technical grounded theory (STGT) approach, involving interviews with 22 software practitioners across three rounds to gather and analyze data [3][5] Usage Statistics - Most participants have used various LLM tools, with ChatGPT being the most frequently used. Approximately 59% of participants interact with LLMs at least six times daily [5][6] Benefits of LLMs - Individual Level: LLMs effectively enhance developers' efficiency and learning capabilities by automating code generation, fixing syntax errors, and providing instant feedback, thus helping maintain a "flow" state [7][9] - Team Level: LLMs reduce collaboration interference and communication costs, allowing junior developers to resolve issues independently before seeking help from colleagues [9] - Organizational Level: LLMs save time and costs for software companies, particularly benefiting small and medium-sized enterprises by enabling them to accomplish more tasks with fewer resources [9] - Societal Level: LLMs foster innovation and entrepreneurship by allowing developers to quickly prototype and learn business and technical knowledge, thus lowering the barriers to starting new ventures [9] Drawbacks of LLMs - LLMs can generate erroneous code or suggestions, which may slow down progress and require additional time for validation. Over-reliance on LLMs can weaken developers' code comprehension and motivation to learn [11][13] - Concerns about copyright and licensing issues have led some companies to prohibit the use of LLMs, while the cost of frequent LLM usage can increase operational burdens [13][14] Recommendations for Developers - Developers are encouraged to experiment with different LLMs to find the best fit for their needs, recognizing that LLMs are statistical tools rather than intelligent agents [14][15] - Maintaining a balanced relationship with LLMs is crucial, where developers trust their capabilities while keeping a rational distance to avoid dependency [14][15]