AI 中文总结
该研究探讨大型语言模型普及下软件工程从代码中心转向规范驱动开发的趋势，提出规范悖论，指出AI生成软件能力越强，越依赖高质量人类规范，强调需求工程的核心地位。
AI 中文摘要
大型语言模型（LLMs）在软件工程中的日益普及，强化了编码活动可实现大规模自动化的预期。然而，这种认知或许只是历史上又一次寻找能消除软件开发固有挑战的解决方案的尝试。本文探讨从以代码为中心的范式向规范驱动开发的转变。我们认为，人工智能减少了编写源代码的部分工作量，但并未消除开发专业软件系统的复杂性，反而将这种复杂性转移到领域理解、需求引出、规范制定、验证、维护及软件演化上。基于此视角，我们探讨需求工程的重新核心地位，及其对生产力和软件质量的影响，以及与自动化偏差、歧义传播、规范过拟合和规范债务累积相关的风险。最后，我们提出规范悖论：人工智能系统自动生成软件的能力越强，对人类生成的正确、完整、可验证且可解释的规范的依赖就越大。我们得出结论，软件工程的未来不仅取决于机器生成代码的能力，还取决于人类正确规范、评估和演化拟构建内容的能力。
英文摘要
The growing adoption of Large Language Models (LLMs) in Software Engineering has reinforced the expectation that coding activities can be largely automated. However, this perception may represent yet another historical search for a solution capable of eliminating the inherent challenges of software development. This article discusses the transition from a code-centered paradigm to Specification-Driven Development. We argue that artificial intelligence reduces some of the effort associated with writing source code, but it does not eliminate the complexity of developing professional software systems. Instead, it shifts this complexity toward domain understanding, requirements elicitation, specification development, validation, maintenance, and software evolution. Building on this perspective, we discuss the renewed centrality of Requirements Engineering, considering its implications for productivity and software quality, as well as risks associated with automation bias, ambiguity propagation, Specification Overfitting, and the accumulation of Specification Debt. Finally, we propose the Specification Paradox: the more capable artificial intelligence systems become at automatically generating software, the greater the dependence on correct, complete, verifiable, and explainable human-produced specifications. We conclude that the future of Software Engineering will depend not only on machines' ability to generate code, but also on humans' ability to correctly specify, evaluate, and evolve what is intended to be built.
