Human-AI Collaboration and the Transformation of Software Engineering Work
2026-06-02 • Software Engineering
Software Engineering
AI summaryⓘ
The authors explain that software engineering is changing because new AI systems can write and manage code on their own. Instead of just writing code themselves, engineers now focus on working with these AI tools, checking their work, and overseeing their behavior. They identify three ways software development is done today: old-fashioned coding, AI-assisted coding, and AI-driven coding. The paper offers a framework describing the new skills engineers will need, such as technical knowledge, teamwork, and governance. The authors suggest the main value of engineers will shift from writing lots of code to guiding AI and making smart decisions.
Generative AIAgentic AISoftware engineeringHuman-AI collaborationAutonomous coding agentsVerification and validationGovernanceCompetency frameworkParadigm shiftSocio-technical systems
Authors
Mamdouh Alenezi
Abstract
The integration of Generative AI (GenAI) and Agentic AI into software development is reconfiguring software engineering from an activity centered on human authorship of code into a discipline centered on directing, verifying, and governing autonomous and semi-autonomous systems. Drawing on a curated, multi-source evidence base of recent peer-reviewed and archival studies -- including large-scale empirical observations of autonomous coding agents contributing hundreds of thousands of pull requests to open-source repositories -- this paper synthesizes how the locus of engineering work is shifting from individual coding productivity toward human--AI collaboration, agent orchestration, verification and validation, governance, and socio-technical systems thinking. We adopt a structured interpretive synthesis to characterize three coexisting paradigms: Traditional, Generative AI-Enabled, and Agentic AI-Enabled software engineering. We map which traditional activities are being automated, which are being augmented, and which are newly emerging, and we trace plausible role trajectories over the next decade. The paper's principal contribution is an original, theory-driven competency framework that organizes the capabilities required of future engineers into five interacting categories -- % technical, cognitive, socio-technical, governance, and organizational -- % operationalized through a competency matrix and a transformation framework linking paradigm shifts to capability demands. We derive nine empirically testable propositions and articulate implications for theory, industry workforce transformation, university curricula, and organizational leadership. We argue that, as code becomes abundant, the durable value of the software engineer increasingly resides in intent specification, critical judgment, and accountable oversight rather than in the sheer volume of code produced.