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Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Continuous integration (CI) means integrating code changes frequently and using automated builds and tests to provide prompt feedback. Continuous delivery extends that automation until a change is ready to release; continuous deployment goes further and automatically puts qualifying changes into use. A sound CI/CD pipeline builds a traceable artifact, checks it, promotes it through appropriate environments, and deploys under explicit safeguards.
CI, continuous delivery, and continuous deployment
Continuous integration
CI is a development practice, not simply a product or a workflow file. Developers integrate changes into a shared version-controlled codebase frequently, and automated builds and checks reveal problems while they are still relatively easy to diagnose. Checks can include linting, security analysis, coverage reporting, and functional tests. They typically run on pushes and other repository events.
Continuous delivery
Continuous delivery automates the path from integrated change through packaging and validation so that software is kept in a releasable state. A team may still require a person to approve a production release. That approval does not make the process “not continuous delivery”: the key is that the software can be released on demand after the required checks.
Continuous deployment
Continuous deployment automatically deploys qualifying changes without a manual production-release decision. Teams commonly use “CD” to mean either delivery or deployment, so state which practice you mean when documenting a pipeline. DORA describes delivery as the ability to release changes quickly, safely, and sustainably on demand. In a 2022 DORA report, Dave Farley, coauthor of Continuous Delivery, summarized the principle as: “The fundamental tenet of continuous delivery (CD) is to work so that our software is always in a releasable state.”
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What a practical pipeline does
Adapt the stages to your application and risk; there is no one pipeline layout that suits every team. A useful baseline is:
- Receive a change. Trigger checks when a change is pushed, opened for review, or otherwise enters the team’s integration process.
- Build and run fast checks. Compile or otherwise build the software, then run quick, high-value checks such as linting and unit tests. Fail early with output that points to the problem.
- Package a versioned artifact. Create the deployable output once and identify it by a stable version or digest, together with its source revision and build provenance.
- Run deeper checks. Execute integration, functional, security, and—where appropriate—performance checks. Choose these based on the system’s failure modes and risk rather than adding tests without an actionable purpose.
- Promote the same artifact. Move the validated artifact through suitable test or staging environments. Avoid silently rebuilding different bits for each environment; keep configuration separate from the artifact where the architecture permits.
- Apply the release policy. Require review or approval where warranted, then deploy using a rollout plan and monitor service health.
- Feed results back. Make failures, deployment identity, and health signals visible to the people who can act on them.
GitHub’s CI documentation describes checks such as linting, security checks, coverage, and functional tests, while its deployment documentation covers environments, branch restrictions, approval controls, secrets, triggers, and concurrency. Google Cloud’s CI/CD explainer, published in 2021, frames CI as getting feedback early and often; its secure-pipeline guidance was last reviewed on 2024-10-29. These are useful conceptual and product-documentation references, not a universal prescription for stage names or timing.
Choose checks for fast, useful feedback
Start with checks that catch important regressions quickly. A failed check should explain what failed and provide enough context to reproduce or investigate it. Add broader checks when they address risks that fast checks cannot cover.
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- Build and static checks: confirm the change can be built and flag issues such as formatting or known security problems.
- Unit tests: exercise small units of behavior and provide a quick signal about local regressions.
- Integration and functional tests: check interactions across components or user-visible behavior that unit tests cannot establish.
- Security checks: examine code and relevant dependencies or artifacts, with findings routed for review rather than treated as a checkbox.
- Performance checks: use where performance regressions are consequential and the measurement is relevant to the deployed system.
The cited guidance identifies these kinds of checks but does not establish a single test pyramid or a universal time target. Set service-appropriate expectations, then track whether pipeline duration and failure messages help or hinder the team’s response.
Make artifacts traceable and releases controlled
A pipeline should let an operator answer: what source revision produced this deployment, which artifact was deployed, which checks passed, and who or what authorized the release? Google Cloud’s deployment guidance emphasizes repeatability and tracing deployments to code or input artifacts. Preserve that chain across build, storage, promotion, and deployment rather than relying on a job log that disappears or an ambiguous tag.
Pick rollout controls based on how much of the system could be affected and how quickly you can detect and stop a bad release. Canary and blue/green rollouts are examples of staged deployment approaches, not guarantees against incidents.
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- Blast radius: can you limit exposure to a subset of users or traffic?
- Health signals: do you have timely, trustworthy indicators that the release is working?
- Stop or reversal path: can the rollout be halted or the application version restored quickly?
- Compatibility: will old and new application versions coexist safely with the database, APIs, and queued work during rollout?
- Operational fit: can the team support parallel versions, traffic routing, and the added complexity?
Do not assume that reverting application code reverses every consequence. A destructive schema migration, data rewrite, or external side effect may persist after a code rollback. Plan backward-compatible migrations and a data recovery path as part of release engineering, and observe the service after deployment.
Secure the pipeline as production infrastructure
A pipeline often has access to source code, packages, build infrastructure, artifact storage, and deployment credentials. Compromise of its configuration or execution environment can become a route to connected production resources. Google Cloud’s secure-pipeline guidance treats the source, libraries, container images, artifact stores, and artifact-producing systems as part of the trust graph.
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- Limit permissions: give each job only the identities and resources it needs, and avoid broad credentials shared across unrelated stages.
- Separate environments: use distinct controls for development, staging, and production; require stronger review for sensitive targets.
- Protect secrets: store credentials in the platform’s secret mechanism, restrict which workflows can access them, and do not print them into logs.
- Verify provenance: GitHub recommends artifact attestations to establish build provenance and verify consumed software. Attestation helps answer where an artifact came from; it does not prove that its code is safe.
- Prefer short-lived identity where supported: GitHub documents OpenID Connect for authenticating workflows with supported cloud providers. Scope the trust relationship to the intended repository, workflow, and target.
- Control concurrency: prevent overlapping deployments when concurrent jobs could race or apply changes in an unsafe order.
- Audit the path: retain enough information to connect a deployment to its change, artifact, checks, and approval.
Google Cloud describes centralized “push” pipelines and decentralized “pull” agents as architectural choices: centralized control can simplify governance, while pull agents place more deployment components in local environments. Neither arrangement is automatically secure; choose according to environment boundaries and the team’s ability to operate and protect the components.
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Select a rollout strategy that matches the service
Canary and blue/green deployments are two common patterns. In a canary, expose a limited portion of traffic or users to the new version before widening the rollout. In blue/green, maintain two deployment environments and switch traffic between them. Actual mechanics depend on the infrastructure, and neither pattern removes the need for health checks, compatible changes, or an incident response plan.
Use a manual production approval when a person needs to assess business timing, compliance, or operational risk. If the team can define reliable automated conditions and safely reverse a rollout, more of the release can be automated. The right goal is a controlled release process, not eliminating every human decision.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Measure delivery without losing sight of stability
Use measures to locate bottlenecks and balance throughput with stability. DORA’s established delivery guidance names change lead time, deployment frequency, change fail rate, and failed deployment recovery time. DORA’s 2025 year-in-review, updated 2026-01-07, says the software-delivery performance set evolved from four metrics to five. Treat the four-item list as the established earlier set, not the complete current framework; consult DORA’s current definitions before adopting or reporting the full metric set.
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Metrics are signals for investigation, not targets to optimize in isolation. A higher deployment frequency, for example, does not establish that changes are safer; read throughput measures alongside failure and recovery measures and the team’s reliability targets.
DORA’s continuous-delivery guidance summarizes a 2021 report finding that “Teams that meet their reliability targets are three times more likely to have adopted a loosely coupled architecture than low-performing teams.” This is an association reported by DORA, not proof that architecture alone causes the result or a forecast for an individual team.
Choose pipeline tooling by operational fit
CI jobs can run on hosted or self-hosted runners. Deployment can use a centralized pipeline or local pull agents. Examples named in the guidance include GitHub Actions, Jenkins, and GitLab; these are examples, not a ranked recommendation. Compare options against the system and team rather than selecting by feature count alone.
- Repository integration and support for your languages and build process
- Deployment targets, runner control, and the ability to isolate sensitive workloads
- Secrets and identity model, environment gates, and auditability
- Artifact provenance and compatibility with existing storage and promotion practices
- Portability, operating cost, and the team’s capacity to maintain runners and pipeline infrastructure
Whichever tooling you choose, keep workflow configuration reviewable as code, constrain what it can access, and document how operators inspect or stop a deployment.
Optional: capture a deployed page from a pipeline
If a release needs visual evidence—for example, a screenshot of a public page after deployment—a pipeline can request an image and store the response as a build artifact for review. Keep any API key in the CI platform’s secret store, not in source control or a printed command. This is an optional pipeline task, not a substitute for application tests or production health checks.
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Troubleshoot common pipeline failures
- A check fails before deployment: inspect the failed job output and identify whether the cause is a code regression, a flaky test, an unavailable dependency, or a runner problem. Fix or isolate the underlying cause; routinely rerunning failures without diagnosis hides feedback.
- The artifact cannot be found or differs between environments: verify that the build stage published the expected version or digest and that promotion consumes that same artifact. Check permissions and retention in the artifact store.
- A deployment waits for approval: inspect the target environment’s configured reviewers and branch restrictions. Confirm that the change is eligible for that environment and that the approval path has an owner.
- Two deployments interfere: inspect whether jobs can run concurrently against the same target. Add a concurrency limit or serialization where overlapping releases could race.
- A credential is unavailable or rejected: check which stage is allowed to access the secret or identity, whether the workflow’s trust conditions match the cloud provider configuration, and whether the credential is scoped to the intended resource.
- The release is unhealthy after deployment: use service health signals and the deployment’s artifact and revision identity to decide whether to halt, roll forward, or reverse the application version. Assess database and external side effects separately; code rollback alone may not restore them.
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