From Code-Centric to Intent-Centric Software Engineering: A Reflexive Thematic Analysis of Generative AI, Agentic Systems, and Engineering Accountability
- URL: http://arxiv.org/abs/2605.11027v1
- Date: Sun, 10 May 2026 20:45:06 GMT
- Title: From Code-Centric to Intent-Centric Software Engineering: A Reflexive Thematic Analysis of Generative AI, Agentic Systems, and Engineering Accountability
- Abstract summary: Generative artificial intelligence (GenAI) and agentic systems are moving software engineering from code-centric production toward intent-centric human-agent work.<n>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.<n>This study 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.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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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