GenAI让软件工程从写代码转向定义意图，人要管好智能体。
From Code-Centric to Intent-Centric Software Engineering: A Reflexive Thematic Analysis of Generative AI, Agentic Systems, and Engineering Accountability
- 通过分析公开讨论与文献，提炼出人机协作新范式。
- 生成代码成本降低，但意图明确、安全验证更关键。
- 适合关注AI时代软件工程变革的开发者与管理者。
生成式人工智能（GenAI）和代理系统正推动软件工程从以代码为中心的生产模式转向以意图为中心的人机协作，自然语言、代码库上下文、工具、测试和治理共同决定交付成果。现有研究多聚焦代码生成、AI辅助编程和软件代理，但对公众技术话语与同行评审证据如何共同塑造行业转型仍了解不足。本研究采用反思性主题分析（RTA）与解释现象学分析（IPA）方法，整合了软件工程与AI领域的同行评审文献、技术基准、公开演讲、访谈、论文、产品技术公告及知名专家在X平台的讨论内容。通过语料库登记表、编码手册、编码矩阵、主题-来源可追溯表、DOI/参考文献审计和可复现协议进行系统化处理。结果表明，GenAI降低了生成合理代码的成本，但提升了对意图定义、上下文整理、架构知识、验证、安全、溯源、治理和可问责人类判断的需求。研究发现，软件工程正从孤立的代码创作转向监督、验证和管理由人、智能体、工具和证据门控构成的社技术系统。这一转变至关重要，因盲目追求速度可能积累隐性技术债务与责任漏洞，而有限自主性则有助于保障质量、安全、可维护性和信任。
原文摘要 · Abstract (English)
Generative artificial intelligence (GenAI) and agentic systems are moving software engineering from code-centric production toward intent-centric human-agent work in which natural language, repository context, tools, tests, and governance shape delivery. Prior studies examine code generation, AI pair programming, and software engineering agents, but less is known about how public technical discourse and peer-reviewed evidence together frame the profession's near-term transition. This study addresses that gap through a reflexive thematic analysis (RTA) dominant and interpretative phenomenological analysis (IPA) informed public-discourse and document analysis. The corpus combines peer-reviewed software engineering and AI literature, technical benchmarks, public talks and interviews, essays, product-facing technical announcements, and X-originated discourse from prominent AI and software engineering voices. Sources were organized through a corpus register, codebook, coding matrix, theme-to-source traceability table, DOI/reference audit, and reproducibility protocol. The analysis shows that GenAI lowers the cost of producing plausible code while increasing the importance of intent specification, context curation, architecture knowledge, verification, security, provenance, governance, and accountable human judgment. The findings indicate that software engineering is becoming less about isolated code authorship and more about supervising, validating, and governing socio-technical systems of humans, agents, tools, and evidence gates. This matters because speed-focused adoption can accumulate hidden technical debt and accountability gaps, whereas bounded autonomy can preserve quality, security, maintainability, and trust.
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