Summary:
Provide native dashboards and metrics for tracking AI-assisted code contributions in Bitbucket Cloud.

Description:

Currently, Bitbucket Cloud does not have a native feature or dashboard to track AI-generated versus manual code metrics.
From a technical standpoint, Git stores the author/committer identity, timestamp, and the diff, but it does not have a metadata field to record "how" a line was produced.
Once code is committed, an AI-suggested line and a hand-typed line are byte-for-byte indistinguishable within the repository.
Consequently, Bitbucket’s built-in reports and APIs cannot natively classify these contributions.

Suggested Functional Requirements:

• AI Attribution: Ability to identify and label commits or code blocks generated by AI assistants.

• Contribution Metrics: Reporting on the percentage of AI-generated vs. manually written lines of code (LoC) per repository/project.

• Usage Trends: Dashboards showing AI adoption trends across different teams and developers over time.

• Productivity Insights: Correlation data between AI usage and PR cycle times or code review comments.

• API Support: Exportable metrics via Bitbucket API for integration with external analytics tools.

Why is this important?
Enterprise customers need to justify the ROI of AI tool investments and ensure that AI-assisted code meets their internal quality and security standards. Without native tracking, teams are forced to build complex, manual workarounds to extract this data.

Workaround: DX (https://getdx.com/)
While this isn't native to Bitbucket today, Atlassian recently acquired DX, a Developer Intelligence Platform specifically designed to help organizations navigate the shift to AI-augmented engineering.
DX provides specialized measurement for AI adoption and productivity that may provide the insights you are looking for. One can explore their AI measurement capabilities here:
https://getdx.com/ai-measurement/