6 min read
Quick Answer
Which software development metrics should CTOs track in 2026? The answer has evolved: DORA metrics remain foundational (deployment frequency, lead time, change failure rate, and recovery time), but they’re no longer sufficient. The 2025 DORA Report reveals AI tools create a paradox: 7.5% better code quality but 7.2% reduced delivery stability. Modern measurement requires three layers: system metrics (DORA), developer experience metrics (satisfaction, flow state, burnout indicators), and business impact metrics (value delivered, not just velocity). Teams that excel across all three layers are twice as likely to meet organizational performance targets.
The 2026 Metric Landscape: What Changed
The AI Paradox Reshaping Measurement
The 2025 DORA Report uncovered something surprising: AI tools are helping individual developers work better but creating unexpected challenges for team-level metrics.
The measurable improvements:
- 7.5% increase in documentation quality
- 3.4% better code quality
- 3.1% faster code reviews
- 1.8% reduction in code complexity
The concerning pattern:
- 7.2% reduction in delivery stability
- 1.5% reduction in delivery throughput
This means traditional metrics no longer tell the complete story. AI amplifies the strengths of high-performing organizations and the dysfunctions of struggling ones, making measurement frameworks more critical than ever.
Developer Sentiment Shift
According to JetBrains’ 2025 Developer Ecosystem Survey of 24,534 developers, 66% don’t believe current metrics reflect their true contributions. Developers want transparency, constructive feedback, and clarity of goals, not just faster CI pipelines.
The message is clear: if your metrics don’t resonate with developers, they won’t drive improvement.
Layer 1: DORA Metrics (Still Essential)
DORA metrics retain the bedrock for measuring delivery pipeline performance, even as AI changes how work gets done.
The Core Four
| Metric | What It Measures | 2026 Elite Performance | Why It Still Matters |
| Deployment Frequency | How often code reaches production | On-demand (multiple per day) | Reflects team agility and automation maturity |
| Lead Time for Changes | Time from commit to production | Less than 1 hour | Shows pipeline efficiency and batch size |
| Change Failure Rate | % of deployments causing issues | 0-15% | Balances speed with stability |
| Mean Time to Recovery | How quickly you fix failures | Less than 1 hour | Indicates system resilience and incident response |
Critical insight: Elite performers who excel across all four metrics are twice as likely to meet organizational performance targets. Speed and stability aren’t trade-offs, high performers excel at both simultaneously.
What DORA Metrics Actually Tell You
Good DORA metrics indicate:
✅ Healthy CI/CD pipelines
✅ Effective automated testing
✅ Small, manageable changes
✅ Quick incident response
What they DON’T tell you:
❌ Whether developers are satisfied
❌ If you’re building the right features
❌ Team burnout levels
❌ Why productivity stalls
This is why 66% of developers say metrics don’t reflect their contributions. DORA measures delivery mechanics, not developer experience or business value.
Layer 2: Developer Experience Metrics
The SPACE framework (Satisfaction, Performance, Activity, Communication, Efficiency) expanded our view beyond output metrics. By 2026, developer experience has become critical to retention and productivity.
Key DX Metrics to Track
Developer Satisfaction (Quarterly Surveys):
- eNPS: How likely developers are to recommend your company
- Flow State: % of time in focused, uninterrupted work
- Tool Satisfaction: Rating of development environment
- Work-Life Balance: Stress levels and time off utilization
Target: 8+/10 satisfaction scores correlate with 35% lower turnover
Efficiency & Flow:
- Pull Request Review Time: How long code sits waiting for review
- Context Switching: Interruptions per day/week
- Build Times: Local development speed bottlenecks
- Blocked Time: Hours waiting on dependencies
Example from Uber: Their Local Developer Analytics found build times were the #1 productivity bottleneck.By tracking command-level data, they identified where developers lost time and saved 4,167 hours daily across 5,000 engineers with targeted optimizations.
Why This Matters for Staffing
When evaluating nearshore partners or hiring models, ask:
- What’s their average PR review time? (Target: <4 hours)
- Do they measure developer satisfaction? (Target: quarterly surveys)
- How do they prevent burnout? (Target: <10% time working outside preferred hours)
Great nearshore teams track these metrics proactively; it’s a quality signal.
Layer 3: Business Impact Metrics
DORA tells you how fast you ship. Developer experience tells you if your team is sustainable. But neither tells you if you’re shipping value.
Metrics That Connect Engineering to Business
Value Delivered:
- Features Adopted: % of shipped features used by customers
- Time to Customer Value: Days from idea to customer benefit
- Revenue per Engineer: Business outcomes per team member (use carefully)
- Customer-Reported Issues: Quality perception
Strategic Alignment:
- Percentage of Planned Work: vs. unplanned/reactive work (target: 70/30)
- Technical Debt Ratio: Time spent on debt vs. new features
- Rework Rate: % of work requiring significant revision
Real-world benchmark: High-performing teams spend 70% on planned work and 30% on unplanned. If you’re 50/50 or worse, your staffing strategy should address this imbalance.
Mini Q&A: Metrics in Practice
Q: Should we track individual developer metrics like lines of code or commits?
A: No. Individual output metrics are easily gamed and toxic to team culture. Focus on team-level metrics and use 1-on-1s for individual performance. As Nicole Forsgren notes in Accelerate, successful teams measure systems, not individuals.
Q: How often should we review metrics?
A: DORA metrics weekly for trends.Developer experience: quarterly surveys with monthly pulse checks. Business impact: monthly or by sprint.The 2025 DORA research recommends monthly metrics reviews where engineering leaders collaborate to unblock workflows.
Q: What if our metrics look good but productivity feels low?
A: You’re probably measuring the wrong things. Good DORA metrics with poor productivity suggestion: (1) too much unplanned work, (2) developer dissatisfaction not captured in metrics, or (3) shipping features no one uses.Add Layer 2 and 3 metrics.
Staffing Implications: What Metrics Tell You About Hiring Needs
When Metrics Indicate You Need MORE Staff
- Lead time increasing despite same deployment frequency (bottleneck)
- PR review time >24 hours consistently (insufficient senior capacity)
- Unplanned work >40% (need specialized roles like SRE)
- Developer satisfaction dropping (burnout risk, need relief)
When Metrics Indicate You Need DIFFERENT Staff
- High change failure rate (need senior quality focus)
- Long MTTR (need DevOps/platform expertise)
- Low feature adoption (need product-minded engineers)
- Build times increasing (need infrastructure investment)
When Metrics Indicate Process Fixes, Not Hiring
- Good DORA but poor developer satisfaction (process/culture issues)
- High activity but low throughput (too much context switching)
- Improving metrics but high turnover (unsustainable pace)
Key principle: Metrics should guide staffing strategy, not just headcount decisions.
Key Takeaways
- AI creates a measurement paradox: 7.5% better code quality but 7.2% reduced delivery stability. Traditional metrics alone miss this complexity, requiring multi-layer measurement.
- A three-layer framework is essential: DORA (system health), Developer Experience.(sustainability), Business Impact (value delivery), elite teams excel at all three simultaneously
- 66% of developers distrust current metrics, demanding measurement that includes satisfaction, flow state, and meaningful work, not just velocity and output.
Building a nearshore team?
In our staffing model, we track DORA metrics, developer satisfaction, and time-to-productivity for every engagement.
Talk with our experts to learn more about our metrics and discuss how we measure success.