arXiv:2607.05125v1 Announce Type: cross Abstract: This paper presents an exploratory evaluation of how increasing levels of AI autonomy affect software development productivity, requirement adherence, and developer cognitive workload. A team of four developers reimplemented the same full-stack web application across three sequential phases: partial AI-assisted development using GitHub Copilot, an AI-exclusive workflow using GitHub Copilot, and an AI-exclusive workflow using AWS Kiro. Evaluation metrics included development effort (hours), requirement adherence (RITM score), AI-interaction effi
This research is emerging now due to the rapid advancements and widespread adoption of AI-assisted tools in software development, necessitating empirical evaluation of their real-world impact.
A strategic reader should care because quantified insights into AI's impact on software development productivity and cognitive load are crucial for optimizing IT investments and managing talent.
Our understanding of AI's efficiency gains and potential downsides in the software development lifecycle becomes empirically validated across different AI autonomy levels.
- · AI tool providers
- · Software developers (upskilled)
- · Companies adopting AI-driven development
- · Legacy software development firms
- · Developers resistant to AI tools
Increased investment in AI-powered development tools and methodologies across the software industry.
Demand for new developer skill sets focused on AI interaction and oversight rather than pure coding.
Consolidation of software development roles and a focus on AI-orchestrated engineering pipelines.
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Read at arXiv cs.AI