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About this paper
Mamdouh Alenezi
The paper systematically synthesizes evidence on the impact of Generative AI, Large Language Models, and agentic systems across software engineering practice, education, and the workforce. It presents a conceptual framework, a nine-dimension competency model, and a four-phase university curriculum roadmap to guide the transition to an AI-native software engineering paradigm.
Key insights
- AI capabilities in software engineering rapidly progressed from code completion to repository-scale autonomy, shifting human effort towards specifying intent, orchestrating agents, and verifying outcomes.
- Evidence on AI's impact on productivity and quality is contradictory, with observed gains often context-dependent, hidden verification costs, and concerns regarding security vulnerabilities in AI-generated code.
- The 'productivity paradox,' 'competence paradox,' and 'trust paradox' highlight the enduring importance of human judgment, critical evaluation, and calibrated trust in AI-native software engineering.
Summary by alphaXiv.
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