Do these 3 things before closing this tab:
1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsImprove developer experience by making important work faster and easier without trading away quality or wellbeing. Treat it as a system-level performance concern: measure outcomes and friction together, use developer feedback to understand what the numbers miss, and test improvements in small learning loops. A tool rollout or rise in activity counts alone is not proof of better engineering.
What does developer experience have to do with engineering performance?
Developer experience is the set of conditions that shape how developers get work done: whether workflows are clear, tools and systems respond reliably, dependencies are manageable, and the resulting software is dependable. It affects both the flow of work and the effort required to deliver it.
That makes it broader than developer satisfaction and broader than delivery speed. An improvement is incomplete if it helps one team finish coding sooner but creates more review delays, operational risk, rework, or unsustainable workload elsewhere. Likewise, activity measures such as lines changed, commits, or tasks closed do not, by themselves, show that valuable work was delivered.
A useful framework comes from Microsoft Research’s EngThrive, which organizes productivity around Speed, Ease, and Quality, with Thriving as a wellbeing guardrail. Its authors, Brian Houck, Tim Bozarth, David Liu, and Dean Carignan, put it this way: “EngThrive organizes productivity around three dimensions – Speed, Ease, and Quality – with Thriving as a guardrail to ensure developer wellbeing improves alongside performance.” The publication record is dated May 2026. Read the EngThrive publication.
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How should you measure developer experience?
Use a small set of measures that represent meaningful work, then pair system data with developers’ accounts of where work gets stuck. The categories below are practical suggestions based on EngThrive’s dimensions—not a universal metric prescription or benchmark.
| Dimension | What to examine | Question it helps answer |
|---|---|---|
| Speed | Time or flow through a repeatable, meaningful workflow, interpreted at team or system level. | Can developers move this work through the system without unnecessary delay? |
| Ease | Workflow completion success, avoidable waits, repeated support requests, and developers’ reported friction. | How much effort or help does it take to complete the work? |
| Quality | Reliability and change outcomes, including the stability signals relevant to your organization. | Does faster flow still produce dependable changes? |
| Thriving | Developer wellbeing and satisfaction, treated as a guardrail rather than a bonus. | Is the way work is getting done sustainable for developers? |
| Context | Diagnostic telemetry combined with survey feedback and workflow-specific comments. | What might explain a change in the outcome measures? |
Choose measures that fit the workflow and can be interpreted together. EngThrive describes pairing outcome-oriented North Star measures with diagnostic submetrics, using system telemetry and developer surveys for context; it does not prescribe one metric set or threshold for every company. Microsoft’s system is a useful model, not evidence that its exact measures should be copied unchanged.
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Do not turn an individual activity count into a ranking of developers. Such counts can reflect task mix, codebase complexity, or the way work is divided, rather than the value or quality of the result. Use measures to understand system conditions and investigate friction, not to reward a proxy that can be optimized at the expense of the actual work.
How do you turn measurement into improvement?
Start with one recurring workflow that developers find difficult, and run a focused improvement cycle. DORA’s 2024 overview advises teams to establish a baseline, form hypotheses, and measure changes iteratively. As DORA puts it, “Taking an experimental approach to continuous improvement remains essential for modern teams.” See DORA’s 2024 research overview.
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- Choose a workflow. Pick a task that occurs often enough to observe, such as setting up a service, testing a change, or getting a deployment ready. Be clear about where it begins and ends.
- Establish a baseline. Record relevant flow or completion outcomes, diagnostic signals such as waits or support requests, and developers’ descriptions of the friction. Note which teams and cases are represented.
- Write a specific hypothesis. State what change you expect to help and why. For example: “If the service setup guide exposes prerequisites and gives actionable failure messages, fewer teams will need help from the enabling team.”
- Make one focused change. Improve the likely source of friction—perhaps clearer self-service steps, more actionable feedback, or removing an unnecessary dependency—rather than changing several parts of the workflow at once.
- Re-measure and investigate shifts. Compare the same workflow and experience signals with the baseline. Check whether the change helped, harmed, or moved the delay or effort to another team or stage.
- Keep, revise, or reverse the change. Use the evidence to decide what to try next. A local improvement that worsens quality, stability, or wellbeing is not a successful overall result.
Keep the loop small enough that teams can connect a change to its effects. DORA’s findings guide hypotheses and continuous improvement; they are not a guarantee that a particular intervention will produce the same outcome in every organization.
How should platform engineering support developers?
An internal developer platform can improve performance at individual, team, and organizational levels, but DORA’s 2024 research also reports that platforms can potentially decrease throughput and change stability. The practical implication is to evaluate the platform by the work it enables and the tradeoffs it introduces, rather than treating platform adoption as an outcome in itself. DORA’s 2024 overview discusses these findings.
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Begin with self-service workflows that remove recurring dependencies and make the result of a task legible. Then assess whether developers can complete the workflow independently, whether feedback helps them recover from failure, and how delivery outcomes change. Compare experience and delivery signals rather than assuming they move together.
- Check whether teams with different needs can complete common tasks without avoidable handoffs.
- Look at workflow completion and feedback quality, not just whether teams have adopted the platform.
- Monitor delivery speed alongside change stability and other quality signals relevant to your environment.
- Use developer feedback to find workflows where the platform adds steps or shifts work to another team.
DORA’s 2024 report describes research involving more than 39,000 professionals across organization sizes and industries globally. That scale makes the findings useful for informing questions and experiments, but it does not turn an association into a guaranteed causal result for an individual organization. Read the report record from Google Research.
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How should teams evaluate AI tools?
Evaluate AI across the full delivery system, not just the speed of code production. Consider coding, testing, review, security, deployment, and the experience of understanding and maintaining generated work. A faster first draft can still lead to more downstream effort if testing, review, or deployment practices cannot handle the change safely.
DORA’s 2025 State of AI-assisted Software Development report characterizes AI as an amplifier of organizational strengths and dysfunctions. Its published research drew on more than 100 hours of qualitative data and survey responses from nearly 5,000 technology professionals worldwide; those figures describe that 2025 study, not the 2024 DORA report. The finding is a reason to examine the surrounding system, not evidence that AI adoption automatically improves performance. Read DORA’s 2025 report record.
How can developer surveys make measurement more useful?
Surveys help explain what telemetry alone cannot: why developers wait, where instructions are confusing, or whether a workflow feels harder than its completion data suggests. Treat responses as evidence about work conditions and interpret them alongside the relevant system signals.
Google Research describes a quarterly, large-scale developer survey at Google that had been running since 2018, with lessons and refinements accumulated over six years. Its experience supports treating surveys as an ongoing measurement practice, not a one-off sentiment check. It does not establish that every organization should use the same cadence or questionnaire. Read the Google Research paper record.
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