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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsTeams can track software quality without slowing delivery by measuring delivery performance and service health together, automating fast feedback, and using the results to improve a specific constraint—not to set individual quotas. Start with a baseline for one service, add only the measures the team can act on, and check releases in production as well as before deployment.
What to measure: delivery performance and service health
Software quality is not just the absence of defects found before release. A team also needs to know whether changes reach users steadily, how often releases require intervention, and whether the service behaves as users expect. Looking at delivery speed without reliability can reward risky releases; looking at defects alone can encourage slow, heavyweight processes without showing whether those processes help.
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DORA’s guide, updated January 5, 2026, describes five software delivery performance measures. The first three indicate throughput; the last two indicate instability. Use them together for a single application or service, and interpret changes in light of that service’s context.
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| Measure | What it measures | How to interpret it |
|---|---|---|
| Change lead time | Elapsed time from a change being committed to version control until it is deployed to production. | Shows how long a change takes to reach production. A longer interval can point to waiting, batching, or other delivery friction; the number alone does not identify the cause. |
| Deployment frequency | Number of deployments in a period, or the interval between deployments. | Shows the pace at which the service is updated. More frequent releases are not automatically better if they come with greater instability or worse service health. |
| Failed deployment recovery time | Time to recover from a failed deployment that requires immediate intervention. | Shows how quickly the team restores normal service after a qualifying deployment failure. Agree on what counts as a failure and when recovery begins and ends. |
| Change fail rate | Ratio of deployments that require immediate intervention after deployment, often involving a rollback or hotfix. | Shows how often a deployment causes an urgent corrective response. Consistent rules for counting deployments and interventions matter more than apparent precision. |
| Deployment rework rate | Ratio of unplanned deployments caused by a production incident. | Shows how much deployment activity is unplanned incident-driven work. Define which deployments and incidents qualify before looking for a trend. |
DORA describes the current model as an evolution from its original four measures to five. Use the five-measure set when referring to its current guide rather than presenting the older formulation as the current official model. These measures are signals about a delivery system, not a complete definition of product quality: they do not tell a team by themselves whether a critical user journey works or whether a service is meeting its users’ needs.
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Connect delivery measures to what users experience
Internal delivery data becomes more useful when paired with service-level indicators (SLIs), service-level objectives (SLOs), and error budgets. An SLI is an indicator of a service’s behavior, an SLO is the target the team sets for that indicator, and an error budget expresses how much unreliability the team can tolerate while pursuing that objective. Together, they help answer a different question from delivery measures: not only how changes move, but whether the service remains healthy for users.
Choose service indicators that reflect the behavior users depend on. For example, a team might track whether a core request succeeds or how quickly a critical operation responds. The particular indicator and target must fit the service; there is no universal threshold established by the guidance here. Review these signals alongside delivery performance so that a faster deployment pace is not mistaken for progress when users are seeing worse service.
Keep the scope coherent. A measure calculated across several unrelated applications may mix different architectures, release patterns, risk levels, and operating conditions. A service-level view makes it easier to discuss what changed and which team-controlled constraint may explain it. If components are tightly connected, state the boundary clearly rather than treating a broad aggregate as directly comparable to a single service.
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Establish a usable baseline before building a measurement system
Begin with the data the team already has and a conversation about where work waits or fails. DORA cautions that integrations across multiple systems can take enough effort to delay improvement work; precise instrumentation is not automatically worth the initial cost. The baseline is a starting point for asking better questions, not a grade the team must immediately raise.
- Choose one application or service. Name the boundary, production environment, and deployment event the team will count. Avoid pooling unlike services just to produce one organization-wide number.
- Agree on event definitions. Decide when a change is committed, what qualifies as a production deployment, what intervention makes a deployment fail, and what incident-driven deployment counts as rework. Apply those definitions consistently when comparing periods.
- Use available records. Start with existing version-control, deployment, incident, and service-health data where they provide a reasonable picture. Add integrations only when a decision depends on accuracy the existing view cannot provide.
- Record a baseline and discuss friction. Look at the measures together, ask where work is waiting or repeated, and select a constraint to investigate. The first useful outcome is a shared explanation to test, not an arbitrary target.
- Revisit the picture after a change. Use team follow-up and retrospectives to see whether the chosen improvement affected delivery and service health. If it did not, reconsider the explanation or the intervention instead of pressuring people to improve the number.
A short, consistent data set is more useful than a sophisticated dashboard that nobody can interpret or maintain. Where data is incomplete, make the limitation visible and use the trend cautiously; do not imply that a precisely displayed number is necessarily a reliable one.
Keep feedback fast with automated checks
Automated tests and analysis can surface defects while a change is still easy to understand and correct. Google Cloud describes a practice in which unit tests and most integration tests are designed to complete promptly, run incrementally, and execute with high parallelism. That is an example of Google Cloud teams’ approach, not a guarantee that every test suite will be fast or a requirement that every organization adopt the same arrangement.
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Use checks in proportion to the risk and the feedback they provide. Unit tests can check small pieces of behavior; integration tests can exercise interactions between components; static analysis can flag classes of code issues. Track coverage as context about which code paths tests exercise, not as proof that a feature is correct. A high coverage figure cannot establish that tests assert the right behavior.
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- Feedback latency: How soon can the engineer find and diagnose a defect?
- Coverage and relevance: Do checks exercise expected behavior and critical user journeys?
- Production risk: Can the team detect a regression that only appears under production conditions?
- Maintenance overhead: Does the value of the check justify the effort to build, run, and maintain it?
Continuous integration and test automation support this feedback loop, but adding gates without examining delay can create a new queue. Review which failures provide actionable information, how often checks block delivery, and whether the same signal is already available elsewhere. Fix weak or redundant checks rather than removing safeguards indiscriminately.
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Use staged releases and production monitoring
Pre-release tests cannot reproduce every production condition. A deployment can behave differently under real traffic, data, dependencies, or infrastructure conditions, so quality tracking must continue after release. Google Cloud describes staged rollouts, canary analysis, and post-rollout monitoring as ways to identify issues that tests do not expose.
A canary sends a limited rollout to a portion of the production environment and compares its health with a control group. If relevant health signals worsen, pause the rollout or roll it back according to the team’s release procedure. A staged release limits the blast radius while the team learns; it does not eliminate the need to define which signals matter or who responds when they change.
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- Release to a limited portion first. Keep the rollout small enough to limit exposure while producing useful observations against the control group.
- Observe after deployment. Monitor service health during rollout and after it expands; do not treat a successful test run as evidence that production is necessarily healthy.
- Pause or recover when signals fail. Follow the agreed response to halt expansion or roll back, then record whether the event qualifies for the team’s failure and recovery measures.
This process gives the team a timely feedback path at two points: automated checks before deployment and observed service behavior after deployment. Its overhead depends on the service and the monitoring already in place, so start with a limited set of meaningful signals rather than collecting every available metric.
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Turn measures into improvement, not quotas
Metrics become counterproductive when people are expected to hit a number regardless of context. A deployment-frequency quota, for example, can encourage changes to be split or counted in ways that improve the dashboard without improving the software. A lead-time target can invite teams to exclude inconvenient waiting time. A low failure rate can reflect careful releases, but it can also reflect inconsistent incident recording. DORA warns against treating measures as targets, relying on a single metric, and comparing unlike applications.
Use a small set of measures to start a team-level conversation: what is slowing a change, what work is being repeated, and what do users experience when releases occur? Agree on one process or technical constraint to improve, make the change, and revisit the evidence together. Keep the measure definitions stable enough to see a trend; if a definition must change, note where the series changes so an apparent improvement is not mistaken for a real one.
- Do not use service delivery measures as individual performance scores.
- Do not rank teams whose services and operating contexts differ.
- Do not optimize throughput while ignoring failed deployments, rework, or service health.
- Do not delay improvement work to build elaborate data integrations before the team has a decision that needs them.
- Do not treat test coverage or any other single signal as a complete quality verdict.
The useful question is not “How do we make this number go up?” It is “What constraint is preventing this service from delivering reliable changes, and what evidence would show whether our response helped?”
A lightweight operating rhythm
A practical rhythm can fit into work the team already does. During delivery, let automated checks provide fast feedback and use staged rollout monitoring to assess real service behavior. In a team review or retrospective, inspect the five delivery measures alongside relevant service indicators, discuss any change in context, and choose one improvement to test. At the next review, check whether the constraint shifted and whether users saw any corresponding change in service health.
Keep the review focused. A handful of measures that the team understands is preferable to a wall of charts that cannot lead to an action. If a signal does not prompt a useful question or decision, it may not deserve the collection and maintenance effort. If teams need to compare, compare a service with its own history under consistent definitions rather than treating another application’s values as a universal standard.
This approach does not assume that every release can be made safe by adding more approvals, nor that every delay is waste. It puts feedback near the work, limits rollout risk, and makes measurement serve the team’s learning. The result is a delivery system where quality and speed can be considered together rather than traded off on the basis of one number.
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