A data-driven guide to measuring developer productivity in the AI era, cutting through hype to track real impact and ensure sustainable gains.

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Software Engineering Research

DX introduces an AI Measurement Framework for evaluating developer productivity with AI coding tools, focusing on three key dimensions: utilization, impact, and cost. The framework addresses common measurement pitfalls like relying on acceptance rates and emphasizes the importance of tracking both perceived and actual productivity gains. Key insights include treating AI agents as team extensions, expanding the definition of developers to include AI-enabled contributors, and monitoring second-order effects like increased code review overhead. The approach combines tool telemetry, quarterly surveys, and in-workflow sampling to provide comprehensive measurement data while maintaining developer trust through team-level aggregation.

Measuring AI code assistants and agents with the AI Measurement Framework