The Strategic Evolution of Software Engineering in the Generative AI Era
Authors
Department of Mathematics and Computer Science, Alabama State University, Montgomery, AL (United States of America (USA))
Department of Mathematics and Computer Science, Alabama State University, Montgomery, AL (United States of America (USA))
Article Information
DOI: 10.51244/IJRSI.2026.1308000080
Subject Category: Software Development
Volume/Issue: 13/8 | Page No: 951-960
Publication Timeline
Submitted: 2026-08-23
Accepted: 2026-08-28
Published: 2026-09-04
Abstract
The rapid proliferation of Generative Artificial Intelligence (GenAI) across the software development life cycle (SDLC) is driving a fundamental paradigm shift in software engineering. While isolated, task-level acceleration has been demonstrated under controlled experimental conditions, enterprise environments face a pronounced “productivity paradox” characterized by legacy integration friction, codebase volume inflation, and significant verification overhead. This paper investigates the macroeconomic, architectural, legal, and workforce transformations shaping AI-augmented software development. We present the Phased Impact Model for enterprise adoption, delineate a six-layer reference architecture for the Agentic SDLC (A-SDLC) and explore the restructuring of engineering labor and computer science education. Finally, strategic recommendations are outlined to guide engineering organizations in managing systemic complexity and establishing reliable, standard-compliant development pipelines.
Keywords
Generative AI, Agentic SDLC, Software Engineering, Developer Productivity
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