Developer Productivity in the Age of AI

Authors: Andrew Rutherfoord, Delia Popa, Ioannis Loukas Introduction AI coding tools use large language models to generate code to achieve the desired goals of a developer. These tools are increasingly used in the software engineering field, with the goal of accelerating implementation work and reducing developer effort. Despite their rapid adoption, the extent to which these tools improve developer productivity remains unclear. Therefore, we aim to bridge this research gap and study the use of these tools and whether it has a measurable impact on productivity. Background Software productivity encompasses objective dimensions (effort vs. output) and subjective perceptions of efficiency ( al 2025). We focus on objective metrics—commit frequency, loc modified, and code churn—to measure delivery. Code churn is “commonly used to capture the intensity of software changes” (Gomes et al. 2026) and excessive churn in code files can be associated with poor design and technical debt. Therefore,