Trunk-Based Development and Elite Team Performance
Executive summary
Section titled “Executive summary”High-performing software teams tend to converge on trunk-based development (TBD) not because it is fashionable or ideologically pure, but because it minimizes the structural forces that inflate lead time, change failure rate, and mean time to restore (MTTR).
This article explains why that convergence happens. It connects branching strategy to delivery performance, drawing on industry research (notably DORA/Accelerate), operational mechanics, and practical constraints observed in real teams.
This is not a how-to guide, nor a mandate. It is an explanation of cause and effect.
The question this article answers
Section titled “The question this article answers”Why do elite-performing teams tend to use trunk-based development rather than long-lived branching models like GitFlow?
This is a different question from:
- What is trunk-based development?
- Which branching model is better?
Those questions are factual or prescriptive. This one is explanatory.
Branching strategy as a system design choice
Section titled “Branching strategy as a system design choice”Branching strategy is often treated as a workflow preference. In practice, it is a system design decision that shapes:
- How long changes remain invisible to the rest of the system
- How frequently integration happens
- How quickly defects can be detected and reversed
- How confidently teams can deploy under pressure
Long-lived branches and trunk-based development optimize for different failure modes.
What trunk-based development optimizes for
Section titled “What trunk-based development optimizes for”At its core, trunk-based development optimizes for:
- Continuous integration: changes integrate into a shared trunk at least daily
- Short-lived divergence: feature work is isolated briefly (hours or days, not weeks)
- Fast feedback loops: integration failures surface early
- Low-cost rollback: small changes are easier to revert or forward-fix
These properties directly influence delivery metrics.
:::note Perceived safety vs operational safety Perceived safety comes from isolation: long-lived branches, extended stabilization phases, and delayed exposure to risk.
Operational safety comes from fast feedback: small changes, early integration, and the ability to recover quickly when something goes wrong.
Trunk-based development prioritizes operational safety. Long-lived branching models often optimize for perceived safety, which can break down under incident pressure. :::
Delivery performance and DORA metrics
Section titled “Delivery performance and DORA metrics”The DORA metrics provide a useful lens for evaluating branching strategies:
- Lead Time for Changes
- Deployment Frequency
- Change Failure Rate
- Mean Time to Restore (MTTR)
DORA research consistently shows that high-performing teams achieve both speed and stability. The mechanisms that enable this are not accidental.
Branching strategy is one of those mechanisms.
Evidence from research
Section titled “Evidence from research”Multiple years of DORA / Accelerate research show a strong correlation between continuous integration practices and elite delivery performance. Teams that integrate code frequently, keep batch sizes small, and avoid long-lived divergence consistently outperform peers on lead time and MTTR while maintaining low change failure rates.
Trunk-based development is not called out as a mandate in these reports. Instead, it appears implicitly through the practices associated with high performers: short-lived branches, frequent merges to mainline, and rapid validation. Over time, teams optimizing for these outcomes tend to converge on trunk-based development because it structurally reinforces those behaviors.
Lead time: where long-lived branches accumulate cost
Section titled “Lead time: where long-lived branches accumulate cost”Long-lived branches defer integration. Deferred integration creates:
- Larger merge surfaces
- Higher cognitive load during reconciliation
- Increased probability of conflicts
- Delayed discovery of incompatible changes
Each of these extends the time from code complete to code running safely in production.
Trunk-based development reduces lead time by reducing the maximum distance between development and integration.
MTTR: restoring service under pressure
Section titled “MTTR: restoring service under pressure”During an incident, teams need to answer one question quickly:
What changed?
Trunk-based development makes this easier by:
- Keeping changes small
- Ensuring changes are already integrated
- Allowing rapid revert or forward-fix
Long-lived branches often require additional coordination, cherry-picks, or emergency merges—precisely when time and clarity are scarce.
Why GitFlow feels safer (and when it is)
Section titled “Why GitFlow feels safer (and when it is)”GitFlow optimizes for a different context:
- Infrequent releases
- Heavy manual testing phases
- Clear separation between development and release stabilization
In environments where deployments are rare and regression testing is slow or manual, GitFlow can reduce perceived risk.
The tradeoff is structural latency.
As delivery expectations shift from weeks to hours or minutes, that latency becomes a liability.
Elite performance and constraint removal
Section titled “Elite performance and constraint removal”Elite teams are not simply “faster.” They have systematically removed constraints that slow learning and recovery.
Trunk-based development removes one such constraint: delayed integration.
This is why it appears repeatedly—not as a mandate, but as an emergent property of teams optimizing for fast, safe delivery at scale.
What this does not claim
Section titled “What this does not claim”This article does not claim that:
- Trunk-based development is universally correct
- GitFlow is “bad” or irresponsible
- Branching strategy alone determines performance
Delivery performance is multi-dimensional. Branching strategy is one lever among many.
When to treat this as guidance, not dogma
Section titled “When to treat this as guidance, not dogma”Use this explanation when:
- Lead time expectations are shrinking
- Incidents require rapid restoration
- Deployment frequency is increasing
- Manual regression testing becomes a bottleneck
Treat it cautiously when:
- Releases are infrequent by necessity
- Regulatory or operational constraints dominate
- Teams lack automated testing and deployment safety nets
References and further reading
Section titled “References and further reading”- DORA State of DevOps Reports (annual surveys & findings) — https://dora.dev/research/2024/dora-report/ :contentReference[oaicite:0]{index="0"}
- Accelerate: The Science of Lean Software and DevOps by Nicole Forsgren, Jez Humble, and Gene Kim — https://www.goodreads.com/work/editions/57250986-accelerate-building-and-scaling-high-performing-technology-organization :contentReference[oaicite:1]{index="1"}
- Trunk-Based Development publications list — https://trunkbaseddevelopment.com/publications/
(These are starting points; this article synthesizes rather than reproduces them.)