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Shipping an update usually starts with a code or configuration commit, not an immediate production release. Automated checks build and test the change; teams then package a known artifact, assess release readiness, deploy it using a rollout strategy, and monitor what happens. A green CI run means the change passed the checks that ran—it does not, by itself, mean the update is live or defect-free.
What happens after a code commit?
A commit records a change in version control. In a typical delivery pipeline, it triggers continuous integration (CI): automation builds the software and runs fast tests so developers can catch problems early. From there, a change may pass through additional security and policy checks, release preparation, deployment, and production monitoring. The exact stages and gates vary by organization and by the risks of the system.
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1. CI builds and checks the change
CI commonly compiles or otherwise transforms the source, resolves dependencies, and runs quick automated tests. A failure can block later stages under the team’s release policy. DORA recommends small, self-contained changes and short-lived branches; when a build breaks, teams should identify the responsible change and revert it if it cannot be fixed promptly. DORA’s continuous integration guidance explains the practice.
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2. The pipeline creates a deployable artifact
The build produces an artifact: the packaged output that can be tested and promoted through environments. A reliable pipeline identifies and numbers this package, then promotes the same known artifact downstream rather than independently rebuilding different bits for each environment. That helps teams know what they tested and what they are releasing. DORA describes repeatable build packages in its CI guidance, and NIST’s DevSecOps reference model places artifact management within the broader delivery process.
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3. Further tests and security checks assess risk
After initial CI checks, a pipeline may run more extensive functional, integration, regression, smoke, or acceptance tests. Security checks can include static or dynamic analysis, dependency and vulnerability scans, secret detection, infrastructure-as-code checks, or fuzz testing. Which checks are appropriate depends on the application and its risk. These tests provide evidence within their coverage; they cannot prove that a change has no defects. A failed required check should prevent promotion according to the team’s policy.
4. Release readiness is established
Before production, teams may prepare release notes, record which checks passed, move the artifact to an approved repository or environment, coordinate stakeholders, and verify operational readiness. Automation can collect evidence and enforce controls, but it does not necessarily replace a human release decision or authorization. NIST’s reference model describes controls and coordination across the delivery lifecycle.
5. The artifact is deployed
Deployment installs and configures the packaged artifact and its dependencies, then checks that the installation worked. Teams can expose users to the change all at once or use a staged approach such as rolling, blue/green, or canary deployment. These strategies shape how a change reaches production; none removes the need to observe the service afterward.
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Once deployed, teams watch service health, performance, security, and user-facing behavior. If the release causes problems, they follow a response plan: that might mean stopping further rollout, restoring traffic to an earlier version, or taking another corrective action. Recovery is not always a simple code rollback, particularly when a release has changed persistent data.
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Does every commit go straight to production?
No. Continuous integration, continuous delivery, and continuous deployment describe related but distinct practices. DORA defines continuous delivery as the ability to release changes on demand. Continuous integration is one part of that capability; continuous deployment goes further by automatically deploying released artifacts to production.
So a commit can pass CI and still wait for additional tests, a release decision, a scheduled window, or human authorization. “CI/CD” is often used as a broad label, but it does not tell you which gates an organization uses or whether production deployment is automatic. NIST’s SP 800-204D, published February 12, 2024, describes CI/CD pipelines as moving software through stages such as build, test, package, and deploy.
The aim is to make changes safer and easier to release, not simply to increase deployment frequency. DORA cautions: “Increasing the frequency of deployments without improving processes and architecture is likely to lead to higher failure rates and burned out teams.”
How rollout strategies differ
Rollout choices determine how new software is introduced and how teams can respond if its behavior is poor. NIST’s reference model identifies rolling and blue/green approaches, and also lists canary in its deployment-management component. Their practical trade-offs depend on system architecture, risk, and the team’s ability to operate and monitor the release.
| Strategy | How exposure changes | Practical trade-off |
|---|---|---|
| Rolling | Instances or portions of capacity are updated in succession rather than all at once. | Can limit the amount changed at a time, but old and new versions may coexist during the rollout. Monitor compatibility and health while promotion continues. |
| Blue/green | The new version is deployed alongside the current environment, then traffic is switched to it. | Keeping both environments available can make a traffic switch practical, but requires capacity and coordination for parallel environments. |
| Canary | A limited portion of traffic or users receives the new version before broader rollout. | Provides an opportunity to observe behavior before expanding exposure, but requires suitable monitoring and a way to halt or reverse promotion. |
No strategy is universally best. In each case, teams need signals that can stop promotion and a workable way to restore service if the new version degrades it.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What provenance says about a shipped artifact
A deployable artifact has a supply-chain history: its source, dependencies, build process, and publishing steps. Provenance is information about what entity built an artifact, which process it used, and what inputs it used. It can help teams trace where a package came from, but provenance alone does not certify that the software is safe; assurance depends on verification and the strength of the build process.
SLSA’s version 1.0-rc2 security-level specification describes increasing levels of trustworthiness and protection against tampering. It says L1 provenance can help identify the source version and process, while L2 uses a hosted build service that generates and signs provenance. NIST’s DevSecOps reference model also includes artifact signing and verification, provenance generation and verification, security testing, and checks on deployed components. Not every organization implements SLSA or these controls.
Why database changes make recovery harder
Application code and stored data do not always move backward together. Reverting an application version will not necessarily undo a database migration, and changing a schema may affect both old and new application versions. DORA recommends managing schema changes as version-controlled scripts and making them visible across the delivery lifecycle. Teams should plan and test recovery for data changes rather than treating every failed release as a code-only rollback.
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