Engineering Productivity team

1. You are here:
2. Engineering
3. Quality Department
4. Engineering Productivity team

Maintained by:

On this page

• Mission
• KPIs
• OKRs
• Team
  • Members
  • Stable Counterpart
  • Structure
  • Communication
  • Office hours
  • Workstream objectives
  • Areas of Responsibility
  • Meetings
  • Work prioritization
  • Guiding principles
  • Weighting
  • Prioritization activities
  • Projects
  • Metrics
  • Communication guidelines
    • Pipeline changes
      • Critical pipeline changes
      • Non-critical pipeline changes
    • Automated triage policies
  • Asynchronous Issue Updates
    • How to measure confidence?
    • Weekly Epic updates
      • Examples
• Test Intelligence
  • Predictive test jobs via test mapping
  • Fail-fast job
  • Re-run previously failed tests early
  • Selective jobs via pipeline rules
  • Selective jobs via labels
• Experiments

Mission

• Constantly improve efficiency for our entire engineering team, to ultimately increase value for our customer.
• Measure what matters: quality of life, efficiency, and toil reduction improvements with quantitative and qualitative measures.
• Build partnerships across organizational boundaries to deliver broad efficiency improvements.

KPIs

• Master Pipeline Stability
• Review app deployment success rate
• Time to First Failure (TtFF)

OKRs

Objectives and Key Results (OKRs) help align our sub-department towards what really matters. These happen quarterly and are based on company OKRs. We follow the OKR process defined here.

Here is an overview of our current OKRs.

Team

Members

Stable Counterpart

Structure

Communication

Child Pages

GDK

Flaky tests

Workflow Automation

Office hours

Engineering productivity has monthly office hours on the 3rd Wednesday of the month at 3:00 UTC (20:00 PST) on even months (e.g February, April, etc) open for anyone to add topics or questions to the agenda. Office hours can be found in the GitLab Team Meetings calendar

Workstream objectives

The Engineering Productivity team focuses on the following workstreams and the associated Epics with workstream specific vision and objectives.

Areas of Responsibility

• See it and find it: Build automated measurements and dashboards to gain insights into the productivity of the Engineering organization to identify opportunities for improvement.
  • Implement new measurements to provide visibility into improvement opportunities.
  • Collaborate with other Engineering teams to provide visualizations for measurement objectives.
  • Improve existing performance indicators.
• Do it for internal team: Increase contributor and developer productivity by making measurement-driven improvements to the development tools / workflow / processes, then monitor the results, and iterate.
  • Identify and implement quantifiable improvement opportunities with proposals and hypothesis for metric improvements.
  • Automated merge request quality checks and code quality checks.
  • GitLab project pipeline improvements to improve efficiency, quality or duration.
• Dogfood use: Dogfood GitLab product features to improve developer workflow and provide feedback to product teams.
  • Use new features from related product groups (Analytics, Monitor, Testing).
  • Improve usage of Review apps for GitLab development and testing.
• Engineering support: Participate in activities related to Engineering MR Rate and Quality KPIs.
  • #master-broken pipeline monitoring.
  • KPI corrective actions such as Review Apps stabilization.
  • Merge Request Coach for ~"Community contribution" merge requests.
• Engineering workflow: Develop automated processes for improving label classification hygiene in support of product and Engineering workflows.
  • Automated issues and merge requests triage.
  • Improvements to the labelling classification and automation used to support Engineering measurements.
  • See the gitlab-triage Ruby gem, and Triage operations projects for examples.
• Do it for wider community: Increase efficiency for wider GitLab Community contributions.
• Dogfood build: Enhance and add new features to the GitLab product to improve engineer productivity.

Meetings

Engineering Productivity has weekly team meeting in two parts (EMEA / AMER) to allow for all team members to collaborate in times that work for them.

• Part 1 is Tuesdays 11:00 UTC, 04:00 PST
• Part 2 is Tuesdays 22:00 UTC, 15:00 PST

Work prioritization

The Engineering Productivity team has diverse responsibilities and reactive work. Work is categorized as planned and reactive.

Guiding principles

• We focus on OKRs, corrective actions and preventative work.
• We adhere to the general release milestones like %x.y.
• We are ambitious with our targeted planned work per milestone. These targets are not reflective of a commitment. Reactive work load will ebb and flow and we do not expected to accomplish everything planned for the current milestone.
• Priority labels are used to indicate relative priority for a milestone.

Weighting

We follow the department weighting guidelines to relatively weight issues over time to understand a milestone velocity and increase predictability.

When weighting, think about knowns and complexity related to recently completed work. The goal with weighting is to allow for some estimation ambiguity that allows for a consistent predictable flow of work each milestone.

Prioritization activities

Projects

The Engineering Productivity team recently reviewed (2023-05-19) all our projects and discussed relative priority. Aligning this with our business goals and priorities is very important. The list below is ordered based on aligned priorities and includes primary domain experts for communication as well as a documentation reference for self-service.

Metrics

The Engineering Productivity team creates metrics in the following sources to aid in operational reporting.

• Engineering Productivity Dashboard
• Quality Department KPIs
• Broken Master Pipeline Root Cause Analysis
• Time to First Failure
• Test Intelligence Accuracy
• Engineering Productivity Pipeline Health
• Engineering Productivity Jobs Durations
• Engineering Productivity Package And QA Durations
• Issue Triage Dashboard
• Engineering Productivity Sandbox
• GitLab-Org Native Insights
• Review Apps monitoring dashboard
• Triage Reactive monitoring dashboards
  • Overview dashboard
  • Processors dashboard

Communication guidelines

The Engineering Productivity team will make changes which can create notification spikes or new behavior for GitLab contributors. The team will follow these guidelines in the spirit of GitLab's Internal Communication Guidelines.

Pipeline changes

Critical pipeline changes

Pipeline changes that have the potential to have an impact on the GitLab.com infrastructure should follow the Change Management process.

Pipeline changes that meet the following criteria must follow the Criticality 3 process:

• update to the cache-repo job job

These kind of changes led to production issues in the past.

Non-critical pipeline changes

The team will communicate significant pipeline changes to #development in Slack and the Engineering Week in Review.

Pipeline changes that meet the following criteria will be communicated:

• addition, removal, renaming, parallelization of jobs
• changes to the conditions to run jobs
• changes to pipeline DAG structure

Other pipeline changes will be communicated based on the team's discretion.

Automated triage policies

Be sure to give a heads-up to #development,#eng-managers,#product, #ux Slack channels and the Engineering week in review when an automation is expected to triage more than 50 notifications or change policies that a large stakeholder group use (e.g. team-triage report).

Asynchronous Issue Updates

Communicating progress is important but status doesn't belong in one on ones as it can be more appropriately communicated with a broader audience using other methods. The "standup" model used by a lot of organizations practicing scrum assumes a certain time of day for those to happen. In the context of a timezone distributed team, there is no "9am" that the team shares. Additionally, the act of losing and gaining context after completing work for the day only to gain it again to share a status update is context switching. The intended audience of the standup model assumes that it's just the team but in GitLab's model, that means folks need to be aware of where this is being communicated (slack, issues, other). Since this information isn't available to the intended audience, the information needs to be duplicated which at worst means there's no single source of truth and at a minimum means copy pasting information.

The proposal is to trial using an Asynchronous Issue Update model, similar to what the Package Group uses. This process would replace the existing daily standup update we post in Slack with Geekbot. The time period for the trial would be a milestone or two, depending on feedback cycles.

The async daily update communicates the progress and confidence using an issue comment and the milestone health status using the Health Status field in the issue. A daily update may be skipped if there was no progress. Merge requests that do not have a related issue should be updated directly. It's preferable to update the issue rather than the related merge requests, as those do not provide a view of the overall progress. Where there are blockers or you need support, Slack is the preferred space to ask for that. Being blocked or needing support are more urgent than email notifications allow.

When communicating the health status, the options are:

• on track - when the issue is progressing as planned
• needs attention - when the issue requires attention or intervention to keep it on schedule
• at risk - when there is a risk the issue will not be completed according to schedule

The async update comment should include:

• what percentage complete the work is, in other words, how much work is done to put all the required MRs in review
• the confidence of the person that their estimate is correct
• notes on what was done and/or if review has started
• it could be good to specify the relevant dependencies in the update, if there are multiple people working on it

Example:

**Status**: 20% complete, 75% confident

Expecting to go into review tomorrow.

Include one entry for each associated MR

Example:

**Issue status**: 20% complete, 75% confident

Expecting to go into review tomorrow.

**MR statuses**:

- !11111+ - 80% complete, 99% confident - docs update - need to add one more section
- !21212+ - 10% complete, 70% confident - api update - database migrations created, working on creating the rest of the functionality next

How to measure confidence?

Ask yourself, how confident am I that my % of completeness is correct?.

For things like bugs or issues with many unknowns, the confidence can help communicate the level of unknowns. For example, if you start a bug with a lot of unknowns on the first day of the milestone you might have low confidence that you understand what your level of progress is. Your confidence in the work may go down for whatever reason, it's acceptable to downgrade your confidence. Consideration should be given to retrospecting on why that happened.

Weekly Epic updates

A weekly update should be added to epics you're assigned to and/or are actively working on. The update should provide an overview of the progress across the feature. Consider adding an update if epic is blocked, if there are unexpected competing priorities, and even when not in progress, what is the confidence level to deliver by the expected delivery date. A weekly update may then be skipped until the situation changes. Anyone working on issues assigned to an epic can post weekly updates.

The epic updates communicate a high level view of progress and status for quarterly goals using an epic comment. It does not need to have issue or MR level granularity because that is part of each issue updates.

The weekly update comment should include:

• Status: ok, so-so, bad? Is there something blocked in the general effort?
• How much of the total work is done? How much is remaining? Do we have an ETA?
• What's your confidence level on the completion percentage?
• What is next?
• Is there something that needs help/support? (tag specific individuals so they know ahead of time)

Examples

Some good examples of epic updates that cover the above aspects:

• https://gitlab.com/groups/gitlab-org/-/epics/8628#note_1090732793
• https://gitlab.com/groups/gitlab-org/-/epics/5152#note_1029337901

Test Intelligence

As the owner of pipeline configuration for the GitLab project, the Engineering Productivity team has adopted several test intelligence strategies aimed to improve pipeline efficiency with the following benefits:

• Shortened feedback loop by prioritizing tests that are most likely to fail
• Faster pipelines to scale better when Merge Train is enabled

These strategies include:

• Predictive test jobs via test mapping
• Fail-fast job
• Re-run previously failed tests early
• Selective jobs via pipeline rules
• Selective jobs via labels

Predictive test jobs via test mapping

Tests that provide coverage to the code changes in each merge request are most likely to fail. As a result, merge request pipelines for the GitLab project run only the predictive set of tests by default. These include:

• RSpec predictive jobs which runs relevant RSpec tests that are mapped to the code changes
• Jest predictive jobs which runs relevant Jest tests that are mapped to the code changes

See https://docs.gitlab.com/ee/development/pipelines/index.html#predictive-test-jobs-before-a-merge-request-is-approved for more information.

Fail-fast job

There is a fail-fast job in each merge request pipeline aimed to run all the RSpec tests that provide coverage for the code changes, hence are most likely to fail. It uses the same test_file_finder gem for test mapping. The job provides faster feedback by running early and stops the rest of the pipeline right away if any of the fail-fast job tests fail. Take a look at this youtube video for details on how GitLab implements the fail-fast job with test_file_finder. Note that the current design only works with low-impacting merge requests which are only mapped to a small set of tests. If there is a large number of tests that are likely to fail for a merge request, putting them in a single job is not feasible and could result in a long-running bottleneck which defeats its purpose.

See https://docs.gitlab.com/ee/development/pipelines/index.html#fail-fast-job-in-merge-request-pipelines for more information.

Premium GitLab customers, who wish to incorporate the Fail-Fast job into their Ruby projects, can set it up with our Verify/Failfast template.

Re-run previously failed tests early

Tests that previously failed in a merge request are likely to fail again, so they provide the most urgent feedback in the next run. To grant these tests the highest priority, the GitLab pipeline prioritizes previously failed tests by re-running them early in a dedicated job, so it will be one of the first jobs to fail if attention is needed.

See https://docs.gitlab.com/ee/development/pipelines/index.html#re-run-previously-failed-tests-in-merge-request-pipelines for more information.

Selective jobs via pipeline rules

The GitLab pipeline consists of hundreds of jobs, but not all are necessary for each merge request. For example, a merge request with only changes to documenation files do not need to run any backend tests, so we can exclude all backend test jobs from the pipeline. See specify-when-jobs-run-with-rules for how to include/exclude CI jobs based on file changes. Most of the pipeline rules for the GitLab project can be found in https://gitlab.com/gitlab-org/gitlab/-/blob/master/.gitlab/ci/rules.gitlab-ci.yml.

Selective jobs via labels

Developers can add labels to run jobs in addition to the ones selected by the pipeline rules. Those labels start with pipeline: and multiple can be applied. A few examples that people commonly use:

• ~"pipeline:run-all-rspec"
• ~"pipeline:run-all-jest"
• ~"pipeline:run-as-if-foss"
• ~"pipeline:run-as-if-jh"
• ~"pipeline:run-praefect-with-db"
• ~"pipeline:run-single-db"

See docs for when to use these pipeline labels.

Experiments

This is a list of Engineering Productivity experiments where we identify an opportunity, form a hypothesis and experiment to test the hypothesis.