Tempo

Resources

Notes on engineering metrics, AI-assisted coding and how Tempo calculates them.

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Engineering metrics

Definitions and measurement of delivery flow and developer productivity.

  • Engineering metrics

    Definitions and interpretation of PR flow, deployment, issue flow, collaboration, and AI adoption metrics.

  • Developer productivity metrics

    Definitions and interpretation of developer productivity metrics across flow, experience, quality, and outcomes.

  • PR cycle time

    The definition and calculation of PR cycle time, p50 and p90, draft time, pickup time, and review time.

AI coding & impact

Attribution methods, adoption rates and their measurement limits.

  • AI coding analytics

    Definitions of AI coding adoption, attribution coverage, and comparisons with PR cycle time, review load, and delivery flow.

  • Measuring the impact of AI coding tools

    A measurement approach covering AI adoption, delivery flow, review load, quality, cost, and the limits of causal inference.

  • AI coding detection methodology

    The evidence, confidence rules, data boundaries, and limitations of AI coding attribution in Tempo.

  • AI commit rate

    The definition, calculation, and interpretation of AI commit rate, including detection coverage and aggregation.

Integrations

Data sources, synchronization and the metrics calculated from them.

  • GitHub integration

    GitHub data sources, synchronization, metric calculations, and AI session data boundaries in Tempo.

  • Jira integration

    Jira Cloud data sources, synchronization, issue-flow calculations, and pull-request links in Tempo.

  • Linear integration

    Linear data sources, synchronization, issue-flow calculations, and project relationships in Tempo.