---
source: arxiv
url: https://arxiv.org/abs/2606.03394v1
published_at: '2026-06-02T09:36:39'
authors:
- Mamdouh Alenezi
topics:
- human-ai-collaboration
- agentic-software-engineering
- code-intelligence
- software-engineering-roles
- ai-governance
- developer-productivity
relevance_score: 0.92
run_id: materialize-outputs
language_code: en
---

# Human-AI Collaboration and the Transformation of Software Engineering Work

## Summary
The paper argues that GenAI and coding agents are moving software engineering work toward intent specification, agent direction, verification, and governance. Its main output is a synthesis-based competency model, not a new coding model or benchmark.

## Problem
- AI coding assistants and agents can write, test, review, and submit software changes, so teams need to know what human engineers should still own.
- The problem matters because faster code production can raise review load, security risk, technical debt, and accountability gaps.
- Existing evidence is split across productivity studies, repository studies, education papers, and governance work, so the paper tries to connect those findings into one account of changing engineering work.

## Approach
- The authors use a structured interpretive synthesis of recent peer-reviewed and archival sources about GenAI, agentic AI, software engineering roles, and competency needs.
- They compare three coexisting modes of work: Traditional Software Engineering, Generative AI-Enabled Software Engineering, and Agentic AI-Enabled Software Engineering.
- They classify engineering activities as automated, augmented, or newly emerging, then map those changes to required human skills.
- They propose a competency model with five categories: technical, cognitive, socio-technical, governance, and organizational capabilities.
- They derive nine testable propositions for future empirical work.

## Results
- The paper cites the AIDev study as key evidence: 456,535 agent-authored pull requests across 61,453 repositories and 47,303 developers, produced by five agents: OpenAI Codex, GitHub Copilot, Devin, Cursor, and Claude Code.
- It reports one AIDev example where a single developer produced 164 agent-authored pull requests in three days, compared with 176 human-authored pull requests over the prior three years.
- It states that some agents close pull requests up to an order of magnitude faster than humans, while agent-authored pull requests are merged less often and tend to make fewer structural code changes.
- It cites Peng et al. reporting about 56% faster completion for developers using an AI pair programmer on a standardized task.
- It cites a workshop study with 22 professional software engineers using ChatGPT for three hours and a survey of 410 developers on AI coding support.
- Its main claimed contribution is conceptual: three work paradigms, a ten-dimension comparison, a five-category competency model, and nine empirical propositions.

## Link
- [https://arxiv.org/abs/2606.03394v1](https://arxiv.org/abs/2606.03394v1)