llvm-premerge-checks/scripts/metrics

Metrics

To measure the impact and usefulness of the pre-merge checks, we want to collect a set of metrics. This doc will summarize the metrics and tools. All of the data shall be collected as time series, so that we can see changes over time.

• Impact - The metrics we ultimately want to improve

  • Percentage of build-bot build on main failing. (Buildbot_percentage_failing)
  • Time to fix a broken main build: Time between start of failing builds until the build is fixed. (BuildBot_time_to_fix)
  • Percentage of Revisions on Phabricator where a broken build was fixed afterwards. This would indicate that a bug was found and fixed during the code review phase. (Premerge_fixes)
  • Number of reverts on main. This indicates that something was broken on main that slipped through the pre-merge tests or was submitted without any review. (Upstream_reverts)

• Users and behavior - Interesting to see and useful to adapt our approach.

  • Percentage of commits to main that went through Phabricator.
  • Number of participants in pre-merge tests.
  • Percentage of Revisions with pre-merge tests executed
  • Number of 30-day active committers on main and Phabricator.

• Builds - See how the infrastructure is doing.

  • Time between upload of diff until build results available.
  • Percentage of Revisions with successful/failed tests
  • Number of pre-merge builds/day.
  • Build queuing time.
  • Individual times for cmake, ninja all, ninja check-all per OS/architecture.
  • Result storage size.
  • Percentage of builds failing.

Requirements

• Must:
  • Do not collect/store personal data.
• Should:
  • Minimize the amount of additional tools/scripts we need to maintain.
  • Collect all metrics in a central location for easy evaluation (e.g. database, CSV files).
• Nice to have:
  • As the data is from an open source project and available anyway, give public access to the metrics (numbers and charts).
  • Send out alerts/notifications.
  • Show live data in charts.

Data sources

This section will explain where we can get the data from.

• build bot statistics

Solution

We need to find solutions for these parts:

• Collect the data (regularly).
• Store the time series somewhere.
• Create & display charts.

Some ideas for this:

• bunch of scripts:
  • Run a bunch of scripts manually to generate the metrics every now and then. Phabricator already has a database and most entries there have timestamps. So we could also reconstruct the history from that.
  • TODO: Figure out if we can collect the most important metrics this way. This requires that we can reconstruct historic values from the current logs/git/database/... entries.
• Jenkins + CSV + Sheets:
  • collect data with jenkins
  • store numbers as CSV in this repo
  • Charts are created manually on Google Sheets
• do it yourself:
  • Collect data with Jenkins jobs
  • Store the data on Prometheus
  • Visualize with Grafana
  • host all tools ourselves
• Stackdriver on GCP:
  • TODO: figure out if we can get all the required data into Stackdriver
• Jupyter notebooks:
  • TODO: figure out how that works