Measure whether the team’s system is getting better at delivering useful, reliable software—not how busy each developer looks. Start with a decision you need to make, choose a small set of team-level delivery and developer-experience measures that can inform it, and use the results to remove friction. Commits, lines of code, and other activity counts are not a complete measure of productivity.
Start with the decision, not a score
Before choosing a metric, write down the question the measurement should answer and what you might do differently depending on the result. Examples include “Where are code changes waiting?” and “Did the new development environment reduce setup friction?” A question such as “How do we score each developer?” starts from individual surveillance rather than an improvement the team can act on.
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DORA’s guidance is to choose a measurement framework in light of organizational goals: frameworks organize broad concepts into measurable elements, but they do not all serve the same purpose. There is no universally valid metric set or individual productivity formula. DORA’s framework-selection guidance was last updated August 26, 2025.
Why activity counts miss the work
Software development is complex, creative work shaped by technical and social conditions. Google Research authors Ciera Nicole and Christopher Jaspan describe measuring developer productivity as “inherently difficult” because it means measuring humans doing a complex, creative task. A count of commits, pull requests, or lines changed records only part of that work; it does not by itself show whether the result was useful, whether quality was sustained, or whether collaboration and interruptions helped or hindered progress.
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Google Research’s 2024 article by Ben Ferrari-Church and Carolyn Egelman presents 30 rigorously tested developer goals and describes combining sentiment and behavioral data. That breadth is a useful reminder that the goals of development work are varied; it is not a ready-made scorecard for every team. The 2023 human-centered paper and the 2024 developer-goals article provide further context.
Choose a framework to fit the question
Frameworks are lenses, not competing universal formulas. Pick one whose focus matches the decision, then select only the measures your team can interpret and use. DORA distinguishes developer experience, product excellence, and organizational effectiveness as different measurement aims and does not recommend one framework over the others.
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| Framework or approach | Useful question | What it can and cannot tell you |
|---|---|---|
| SPACE | What combination of satisfaction and well-being, performance, activity, communication and collaboration, and efficiency and flow should the team understand? | DORA identifies SPACE as a common framework. It encourages a broader view than activity alone; selecting dimensions and measures still depends on the team’s goal. |
| DevEx | Which parts of developer experience and workflow affect getting work done? | DORA identifies DevEx as a common framework. Use it when the question concerns the experience of doing development work, rather than treating it as a complete productivity score. |
| H.E.A.R.T. | How can product-experience concepts be applied to developer-facing tools or platforms? | DORA lists H.E.A.R.T. as a framework and notes that it can be combined with delivery measures in some contexts. It is not a substitute for deciding what outcome matters. |
| DORA software delivery metrics | How is software delivery performance changing? | Useful for delivery-system questions, but not a standalone measure of every aspect of productivity or developer well-being. |
Compare options by the decision they support, whether the focus is developer experience, product excellence, or organizational effectiveness, the type of evidence involved, the collection burden and available data, and whether leaders can act on the result. Organizations differ in their data access and resources, so a framework that works well in one setting may not fit another. See DORA’s discussion of measurement frameworks.
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For a delivery bottleneck, pair a relevant team-level flow or quality signal with developers’ accounts of where work gets stuck. For a developer-experience question, use a lightweight recurring survey or interviews alongside an operational signal related to the change. This pairing is a practical way to connect what the system records with how the work feels; it is not a universal metric package prescribed by DORA.
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What system logs are good for
Logs can provide continuously measured, standardized data at scale, which is useful for tracking operational patterns over time. But logging depends on toolchain observability, and instrumentation choices and interpretation can introduce inaccuracies or bias. As DORA cautions, it is a misconception to assume logs-based metrics are objective. Treat a log as evidence about a process, not a transparent readout of an individual’s value.
What self-reports are good for
Surveys, interviews, focus groups, and diary studies can surface friction and experience that tools do not capture well. They also have limitations: results may be hard to compare or scale, and recall or social-desirability bias can affect responses. Use questions tied to a specific workflow or change, and interpret responses alongside—not in place of—other evidence.
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Set anti-surveillance safeguards before collecting data
DORA discusses strengths and limits of measurement methods, but its framework guidance is not a complete privacy policy. To keep measurement focused on improvement rather than monitoring individuals, establish safeguards that fit your organization’s rules and applicable law:
- Prefer team or system-level views when the improvement opportunity is a shared process, tool, or workflow. This is a recommended safeguard for an anti-surveillance approach, not a rule mandated by a measurement framework.
- Do not turn passive activity records into employee rankings. Use individual conversations for coaching and context rather than treating logs as a scorecard.
- Explain the collection and access. Tell the team what is collected, who can see it, and what decisions the data can—and cannot—support.
- Collect only what answers the stated question. A larger data trail is not automatically more informative, especially when no one can act on it.
- Share findings and interpretation with the team. Invite people to explain unexpected patterns and help identify changes worth testing.
Run a small improvement cycle
- State the question. Name the friction or outcome you want to understand and the decision the answer could inform.
- Choose a few relevant measures. Include an operational signal and, where the question concerns experience, a developer-reported view. Confirm that the data can be gathered responsibly and interpreted in context.
- Establish a baseline. Record the current pattern before changing the process or tool. Note what the measure captures and what it leaves out.
- Make one improvement. For example, address a specific setup bottleneck rather than changing several workflows at once.
- Measure again and discuss the result. Compare the new evidence with the baseline, ask developers what changed, and adjust the intervention or the measure.
This Plan/Do/Check/Adjust cycle follows DORA’s advice to position organizations to act on what they measure. If no one can name an action that a result might support, reconsider whether that measurement is worth collecting.
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Measure AI adoption without confusing activity for value
When introducing AI coding tools, keep baseline measures that still match the goal and add a small number of targeted measures, such as suggestion acceptance, model quality, trust, perceived productivity, or review time. More generated code or faster activity does not establish sustained value or quality on its own.
DORA and Google’s 2025 report describes more than 100 hours of qualitative data and survey responses from nearly 5,000 technology professionals worldwide. Its high-level finding is that AI can amplify existing organizational strengths and dysfunctions. That means an AI tool should be evaluated in the context of the development system around it, not as an isolated productivity switch. Read the report description.
One older adoption statistic illustrates why survey results need their context: in a 2023 Wakefield Research survey commissioned by GitHub of 500 U.S.-based developers at enterprise companies, 92% said they used AI coding tools at work or outside work. This is a dated, geographically and organizationally bounded result—not a current estimate for all developers. The same survey found that 33% said their companies used collaboration and communication as a performance metric; that figure likewise describes those respondents, not a universal practice. GitHub’s account of the survey gives the sample context.
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