Evolving with AI: A Longitudinal Analysis of Developer Logs
This addresses the gap in knowledge about long-term AI adoption in software development for developers and tool designers, though it is incremental as it builds on prior short-term studies.
The study tackled the problem of understanding how sustained AI use reshapes daily coding practices by analyzing two-year telemetry from 800 developers and a survey of 62 professionals, finding that AI users produce more code but also delete significantly more, with survey respondents reporting productivity gains.
AI-powered coding assistants are rapidly becoming fixtures in professional IDEs, yet their sustained influence on everyday development remains poorly understood. Prior research has focused on short-term use or self-reported perceptions, leaving open questions about how sustained AI use reshapes actual daily coding practices in the long term. We address this gap with a mixed-method study of AI adoption in IDEs, combining longitudinal two-year fine-grained telemetry from 800 developers with a survey of 62 professionals. We analyze five dimensions of workflow change: productivity, code quality, code editing, code reuse, and context switching. Telemetry reveals that AI users produce substantially more code but also delete significantly more. Meanwhile, survey respondents report productivity gains and perceive minimal changes in other dimensions. Our results offer empirical insights into the silent restructuring of software workflows and provide implications for designing future AI-augmented tooling.