Three-Phase Evaluation of AI-Assisted Software Development Life Cycle
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 efficiency, and NASA-TLX workload measures. Across phases, higher levels of AI autonomy were associated with reduced development effort, improved requi...
| Authors | Joshua Strubel, Professor Carrie Russell, Carson Crockett et al. |
|---|---|
| Primary category | cs.SE |
| Published | 6 July 2026 |
| Last updated at source | 6 July 2026 |
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