Episode #069: Developer Experience Metrics Beyond DORA
Duration: 14 minutes | Speakers: Jordan & Alex | Target Audience: Platform engineers, engineering leaders, DevOps practitioners
Series: Platform Engineering 2026 Look Forward (Episode 3 of 5)
This is the third episode in our five-part "Platform Engineering 2026 Look Forward Series." We tackle how organizations should measure success in 2026. DORA metrics revolutionized DevOps measurement, but they have a critical blind spot: they tell you how your delivery pipeline is performing, but not how your people are doing. We explore the SPACE framework, DX Core 4, cognitive load measurement, and the HEART framework for platform teams.
Key Takeaways
| Framework | Key Metrics | Focus Area |
|---|---|---|
| DORA | Deployment frequency, lead time, CFR, MTTR | Delivery pipeline performance |
| SPACE | Satisfaction, Performance, Activity, Communication, Efficiency | Multi-dimensional productivity |
| DX Core 4 | Speed, Effectiveness (DXI), Quality, Impact | Unified measurement with oppositional metrics |
| HEART | Happiness, Engagement, Adoption, Retention, Task Success | Platform product-market fit |
The Five-Metric Starter Pack for 2026
- Deployment Frequency (DORA) - Are you shipping?
- Lead Time (DORA) - How fast?
- DXI Survey Score (DX Core 4) - Are developers happy and productive?
- Time to First Deployment (Onboarding) - Cognitive load proxy
- % Time on New Features (Impact) - Business value creation
Key Statistics
| Metric | Data Point | Source |
|---|---|---|
| Cognitive burden impact | 76% of orgs admit architecture creates developer stress | Industry research |
| Platform maturity effect | 40-50% cognitive load reduction | State of PE Vol 4 |
| AI adoption paradox | 1.5% throughput decrease, 7.2% stability decrease | DORA 2025 |
Metrics Anti-Patterns to Avoid
- Lines of code - Measures activity, not outcomes
- Number of commits - Can be gamed easily
- PRs merged per individual - Creates perverse incentives
- Over-optimizing single metrics - Triggers Goodhart's Law
Resources
Full Transcript
Jordan: This is episode three of our five-part Platform Engineering 2026 Look Forward Series. In episode one, we covered agentic AI and the 60/30/10 framework. Episode two explored platform engineering going mainstream. Today, we're tackling a question that every platform team will face in 2026: how do we actually measure success?
Alex: And the uncomfortable truth is that the metrics we've been using for years might be insufficient. DORA metrics revolutionized how we measure DevOps performance, but they have a critical blind spot. They tell you how your delivery pipeline is performing, but they don't tell you how your people are doing.
Jordan: Let me put some numbers on that. A recent study found that 76% of organizations admit their software architecture's cognitive burden creates developer stress and lowers productivity. Meanwhile, high-maturity platform teams report 40 to 50 percent reductions in cognitive load. The difference between those two states isn't captured by deployment frequency or lead time.
Alex: So let's start with what DORA does well, because it's still foundational. The four core DORA metrics are deployment frequency, which measures how often you ship. Lead time for changes, which measures how long from code commit to production. Change failure rate, the percentage of deployments that cause issues. And mean time to recovery, how quickly you fix failures. There's also a fifth metric around reliability and meeting performance goals.
Jordan: These metrics transformed our industry. Before DORA, we had endless debates about what "good" looked like. DORA gave us data-driven benchmarks. Elite performers deploy multiple times per day, have lead times under an hour, change failure rates under 5%, and recover in under an hour.
Alex: But here's the problem. Teams can hit every one of those metrics while their engineers burn out. DORA tells you the what but not the how or the at what cost. A decade of DORA research itself concludes that software development success hinges not just on technical prowess but also on fostering a supportive culture and focusing on developer experience.
Jordan: So the question becomes: what fills that gap? Enter the SPACE framework, developed by Microsoft Research with input from Nicole Forsgren, one of the original DORA researchers.
Alex: SPACE stands for five dimensions of developer productivity. Satisfaction measures how fulfilled developers feel about their work. Performance looks at the quality and impact of work, not just quantity. Activity captures observable actions like commits and code reviews. Communication and collaboration examines team dynamics and information flow. And efficiency and flow focuses on minimal friction and reduced interruptions.
Jordan: What makes SPACE valuable is its acknowledgment that productivity is multidimensional. You can't reduce a developer's contribution to a single number. But SPACE has its own limitations.
Alex: The main criticism is that SPACE supports custom metric creation but lacks clear implementation guidance. If I'm an engineering leader trying to adopt SPACE, I know what I should measure, but I don't necessarily know how to measure it or what "good" looks like for my organization.
Jordan: This is where the newest framework comes in. In late 2024, DX introduced the DX Core 4, developed in collaboration with Nicole Forsgren and Margaret-Anne Storey. The goal was to simplify the fragmented metrics landscape by creating a unified approach that encapsulates DORA, SPACE, and developer experience research into one coherent framework.
Alex: The DX Core 4 has four dimensions, each with key and secondary metrics. First is Speed. The key metric here is diffs per engineer, essentially pull requests or merge requests at the team level. Importantly, this is not tracked at the individual level. Secondary metrics include lead time and deployment frequency, borrowed from DORA.
Jordan: Second is Effectiveness. The key metric is the Developer Experience Index, or DXI, calculated from a research-backed 14-question survey. This is where you capture whether developers feel productive, have the tools they need, and aren't drowning in friction.
Alex: Third is Quality. The key metric is change failure rate from DORA, with secondary metrics like failed deployment recovery time. This keeps teams from sacrificing stability for speed.
Jordan: And fourth is Impact. This is the most unique dimension. The key metric is the percentage of time spent on new capabilities versus maintenance or tech debt. It frames software development in direct business terms.
Alex: What I find clever about the DX Core 4 design is the concept of oppositional metrics. By measuring speed alongside quality, and effectiveness alongside impact, you create natural tensions that prevent gaming. If a team tries to optimize deployment frequency by shipping tiny meaningless changes, their impact score will suffer.
Jordan: Let's talk about cognitive load specifically, because this is platform engineering's core value proposition. The problem we're solving is that DevOps practices often shifted infrastructure complexity onto developers. Modern developers juggle code, builds, infrastructure, monitoring, scaling, security, deployment, and about twelve other concerns. Every context switch destroys productivity.
Alex: The metrics we should track as proxies for cognitive load include time to first deployment for new developers, which is onboarding friction. Provisioning time for new resources. Number of tickets filed per deployment. Context switches per day, if you can measure it. And of course, developer satisfaction surveys.
Jordan: Platform engineering's explicit mission is reducing this cognitive load. Golden paths and self-service reduce mental strain without hiding necessary context. The data shows that high-maturity platforms achieve 40 to 50 percent reductions in cognitive load for developers.
Alex: There are also informal warning signs that cognitive load is too high. Watch for increasing employee attrition rates. Declining quality of work. Fewer developers volunteering for additional tasks. And the ratio of time in meetings versus time coding trending in the wrong direction.
Jordan: Let's talk about one more framework that's useful for platform teams specifically. Google's HEART framework was originally designed for product metrics, but it translates well to internal platforms.
Alex: HEART stands for Happiness, Engagement, Adoption, Retention, and Task Success. For platform teams, Happiness is developer satisfaction scores. Engagement is how often developers use platform features. Adoption is how many teams have onboarded. Retention is whether teams stay on the platform or work around it. And Task Success is completion rates for platform workflows.
Jordan: This helps platform teams think like product teams. You're measuring whether your internal customers are getting value, not just whether the platform technically works.
Alex: Now let's talk about what not to measure, because metrics anti-patterns are just as important as good metrics.
Jordan: The classic vanity metrics to avoid include lines of code written, number of commits, pull requests merged per individual developer, and time in office. These either measure activity instead of outcomes, or they create perverse incentives.
Alex: Gaming is always a risk. If you over-emphasize deployment frequency, teams make smaller and smaller deploys to hit the number. If you over-emphasize lead time, teams skip code review. If you obsess over change failure rate, teams avoid innovation and only ship safe, boring changes.
Jordan: This is Goodhart's Law in action. When a measure becomes a target, it ceases to be a good measure. The solution is the oppositional metrics approach that DX Core 4 uses. Balance speed with quality. Balance effectiveness with impact. No single metric should dominate.
Alex: So how do you actually implement this? My recommendation is a layered approach. Start with DORA as your baseline. You need delivery metrics. You need to know if you can ship reliably. Then add a developer experience survey, whether that's DXI or something you build yourself. Run it quarterly to track trends.
Jordan: Track cognitive load proxies. Time to first deployment for new hires. Number of tickets required per deployment. Self-service versus ticket-based requests. These tell you if your platform is actually reducing friction.
Alex: Measure platform adoption directly. Are developers using your tools? Are they finding workarounds? Adoption rate is one of the most honest signals of platform value.
Jordan: And don't forget business impact. What percentage of engineering time goes to new features versus maintenance versus tech debt? If platform engineering is working, that ratio should improve over time.
Alex: Here's a balanced scorecard for platform teams. In the Speed category, track deployment frequency and lead time. In Quality, track change failure rate and mean time to recovery. In Experience, track your DXI score and developer attrition rate. In Impact, track percentage of time on new features and hours spent on tech debt.
Jordan: I want to mention one finding from the 2025 DORA report on AI because it's counterintuitive and relevant. As AI adoption increased, delivery throughput decreased by an estimated 1.5%, and delivery stability decreased by 7.2%.
Alex: That seems backwards. Shouldn't AI make us faster?
Jordan: The explanation is that AI amplifies. It magnifies the strengths of high-performing organizations and the dysfunctions of struggling ones. Teams with solid foundations like continuous integration, automated testing, and good documentation get value from AI. Teams lacking those foundations experience disruption instead.
Alex: The implication for platform teams is that AI won't fix broken metrics. If your developers are frustrated and your platform is creating friction, adding AI coding assistants won't solve the underlying problems. You need the foundations first.
Jordan: Let's close with a practical five-metric starter pack for platform teams in 2026.
Alex: Metric one: Deployment Frequency from DORA. This tells you if you can ship. Metric two: Lead Time from DORA. This tells you how fast you can ship. Metric three: DXI Survey Score from DX Core 4. This tells you if developers are happy and productive. Metric four: Time to First Deployment for new hires. This is your cognitive load proxy. Metric five: Percentage of Time on New Features. This is your business impact measure.
Jordan: Five metrics. Balanced across delivery, experience, and impact. Start there, then expand as you mature.
Alex: And here's the meta-point. The metrics you choose signal what you value. If you only measure delivery metrics, you're telling your organization that speed is all that matters. If you add experience metrics, you're saying people matter too. If you add impact metrics, you're connecting engineering work to business outcomes.
Jordan: Platform engineering in 2026 requires all three. The organizations that get this right will build platforms that developers love, that ship reliably, and that create measurable business value.
Alex: Which brings us to next episode. If we're measuring developer experience and platform maturity, what about the infrastructure underneath? In episode four, we explore why Kubernetes entering the boring era might be the best thing to happen to platform engineering.
Jordan: The measure of success for infrastructure is becoming invisible. We'll see you next time.