Software engineering productivity has never been more critical—or more complex to measure. With remote and hybrid work reshaping how teams collaborate, traditional metrics like lines of code or hours logged fail to capture the nuanced reality of modern development work. (Worklytics)
The good news? Fresh benchmark data from industry leaders provides unprecedented clarity on what constitutes competitive productivity scores for software engineers in 2025. LinearB's latest engineering benchmarks, analyzing over 6.1 million pull requests from 3,000 teams across 32 countries, reveal elite, good, and fair performance ranges across key development lifecycle metrics. (LinearB) Meanwhile, Clockwise's focus-time dataset covering 80,000 engineers illuminates the collaboration-productivity balance that separates high-performing teams from the rest.
This analysis translates these comprehensive datasets into actionable productivity score bands, explaining how platforms like Worklytics map code throughput, collaboration load, and focus time into weighted scores that engineering leaders can use to benchmark their teams. (Worklytics) Whether you're looking to justify headcount, optimize team performance, or simply understand where your engineers stand relative to industry peers, these 2025 benchmarks provide the data-driven foundation you need.
The shift to hybrid work has fundamentally changed how we measure engineering effectiveness. Traditional velocity metrics—story points completed, commits per day, or feature delivery rates—provide an incomplete picture of team performance. (Worklytics) Modern productivity measurement requires a more holistic approach that balances technical output with collaboration efficiency and sustainable work practices.
Code Throughput & Quality
This pillar encompasses the technical aspects of software delivery: cycle time from first commit to production, pull request size and review efficiency, and deployment frequency. LinearB's 2025 benchmarks show that elite teams maintain cycle times under 2.5 days while good teams average 4-7 days. (LinearB)
Collaboration Load & Efficiency
With hybrid work elongating the workday span while changing work intensity patterns, measuring collaboration efficiency has become crucial. (Worklytics) This includes meeting frequency and duration, asynchronous communication patterns, and the balance between collaborative work and focused development time.
Focus Time & Work-Life Balance
Sustainable productivity requires adequate focus time for deep work. Research shows that excessive collaboration and messaging can reduce productivity by up to 25%, making focus time measurement essential for long-term team health. (Worklytics)
LinearB's 2025 benchmarks represent the most comprehensive view of software engineering performance to date, incorporating 20 metrics spanning the full Software Development Life Cycle plus 7 new metrics for enhanced visibility. (LinearB)
| Metric Category | Elite (90th percentile) | Good (75th percentile) | Fair (50th percentile) |
|---|---|---|---|
| Cycle Time | < 2.5 days | 4-7 days | 8-15 days |
| PR Size | < 150 lines | 150-300 lines | 300-500 lines |
| Merge Time | < 4 hours | 4-24 hours | 1-3 days |
| Deploy Frequency | Multiple per day | Daily | Weekly |
| Mean Time to Restore | < 1 hour | 1-4 hours | 4-24 hours |
Clockwise's analysis of 80,000 engineers reveals critical insights about the relationship between focus time and productivity. Teams with 4+ hours of daily focus time consistently outperform those with fragmented schedules, yet the average engineer receives only 2.5 hours of uninterrupted work time per day.
Elite Focus Time Patterns:
Collaboration Efficiency Benchmarks:
Worklytics leverages existing corporate data to deliver real-time intelligence on how work gets done, analyzing collaboration, calendar, communication, and system usage data without relying on surveys. (Worklytics) The platform's productivity scoring methodology combines multiple data streams into weighted composite scores that reflect true engineering effectiveness.
Technical Output (40% weight)
Collaboration Efficiency (35% weight)
Work Sustainability (25% weight)
Worklytics announced version two of its Workplace Metrics Benchmark in February 2025, incorporating fully sanitized and aggregated data from millions of digital work accounts across several hundred thousand individuals. (Worklytics) This expanded dataset includes new work type profiles specifically describing work habits for Software Engineers, providing more accurate benchmarking capabilities.
Based on the combined LinearB and Clockwise datasets, along with Worklytics' benchmarking methodology, here are the 2025 productivity score ranges:
| Score Range | Performance Level | Characteristics |
|---|---|---|
| 85-100 | Elite | Cycle time < 2.5 days, 4+ hours focus time, minimal context switching |
| 70-84 | High | Cycle time 2.5-4 days, 3-4 hours focus time, efficient collaboration |
| 55-69 | Good | Cycle time 4-7 days, 2-3 hours focus time, balanced workload |
| 40-54 | Fair | Cycle time 7-15 days, 1-2 hours focus time, high meeting load |
| Below 40 | Needs Improvement | Cycle time > 15 days, fragmented focus time, collaboration overload |
Jellyfish Research's analysis of 78,000 engineers and 11,000 teams shows encouraging trends in software development productivity. Innovation Allocation—time devoted to new features and business-forward work—has increased by 31%, while Issue Cycle Time has decreased by 23%. (Jellyfish) These improvements suggest that teams are becoming more efficient at delivering value while reducing time-to-market.
Startup Teams (< 50 engineers)
Mid-size Companies (50-500 engineers)
Enterprise Teams (500+ engineers)
LinearB's global dataset spanning 32 countries reveals interesting productivity variations. Teams in North America and Europe show similar productivity patterns, while Asia-Pacific teams demonstrate higher code review thoroughness but longer cycle times. (LinearB)
Remote and hybrid teams face unique productivity challenges. Over 58% of the workforce now engages in remote work, increasing reliance on digital collaboration tools and changing traditional productivity measurement approaches. (Worklytics)
True productivity is about efficiency, effectiveness, and sustainability—not just raw output metrics. (Worklytics) When setting productivity targets for your engineering team, consider these evidence-based approaches:
Start with Current State Assessment
Before implementing productivity improvements, establish baseline measurements across all three pillars: technical output, collaboration efficiency, and work sustainability. Worklytics provides solutions for analyzing work patterns and identifying improvement opportunities. (Worklytics)
Focus on System-Level Improvements
Individual productivity scores matter less than team-level performance patterns. Microsoft's workplace analytics research found that many teams were spending too much time in meetings, reducing deep work time. By making meetings more structured, they improved overall productivity. (Worklytics)
Implement Gradual Changes
Productivity improvements require cultural and process changes that take time to embed. Target 5-10 point improvements in productivity scores over 6-month periods rather than dramatic short-term changes that may not be sustainable.
Manager effectiveness significantly impacts team productivity scores. Top-performing managers provide regular coaching, define reasonable team norms, support without micromanaging, and maintain consistent 1:1 schedules. (Worklytics)
Key Manager Behaviors for Productivity:
To help teams benchmark their productivity against 2025 industry standards, here's a simplified scoring framework you can implement:
Cycle Time Score:
- Elite (< 2.5 days): 15 points
- Good (2.5-7 days): 10 points
- Fair (7-15 days): 5 points
- Poor (> 15 days): 0 points
PR Quality Score:
- Small PRs (< 150 lines): 10 points
- Medium PRs (150-300 lines): 7 points
- Large PRs (300-500 lines): 4 points
- Very Large PRs (> 500 lines): 0 points
Deployment Frequency Score:
- Multiple daily: 15 points
- Daily: 10 points
- Weekly: 5 points
- Less than weekly: 0 points
Meeting Load Score:
- Optimal (12-15 hrs/week): 15 points
- Good (15-20 hrs/week): 10 points
- High (20-25 hrs/week): 5 points
- Excessive (> 25 hrs/week): 0 points
Response Time Score:
- Fast (< 2 hours): 10 points
- Good (2-6 hours): 7 points
- Slow (6-24 hours): 4 points
- Very Slow (> 24 hours): 0 points
Cross-team Collaboration Score:
- High engagement: 10 points
- Moderate engagement: 7 points
- Low engagement: 4 points
- Isolated: 0 points
Focus Time Score:
- Excellent (4+ hours): 15 points
- Good (3-4 hours): 10 points
- Fair (2-3 hours): 5 points
- Poor (< 2 hours): 0 points
Work-Life Balance Score:
- Healthy boundaries: 10 points
- Occasional overtime: 7 points
- Regular overtime: 4 points
- Chronic overwork: 0 points
Hybrid work has changed the shape of the workday, elongating the span while changing intensity patterns. Worklytics has introduced new models to understand these changes, including Workday Intensity measured as time spent on digital work as a percentage of overall workday span. (Worklytics)
Optimal Workday Intensity Patterns:
Worklytics' four new work modeling approaches include Team Health metrics that correlate strongly with productivity outcomes. (Worklytics) Teams with high health scores—characterized by balanced workloads, regular communication, and sustainable practices—consistently achieve higher productivity scores.
Team Health Indicators:
The integration of AI tools and automation is reshaping productivity benchmarks. Teams effectively leveraging AI for code generation, testing, and documentation show 15-25% improvements in cycle time while maintaining code quality. However, the learning curve and tool integration overhead can temporarily reduce productivity scores during adoption phases.
Data Collection
Implement comprehensive measurement across all productivity pillars. Worklytics provides solutions for remote and hybrid work analytics, AI adoption tracking, and organizational network analysis to establish accurate baselines. (Worklytics)
Benchmark Comparison
Compare your team's metrics against the 2025 industry benchmarks. Worklytics' version 2 benchmark dataset allows customers to configure benchmark exports and compare against industry peers. (Worklytics)
Meeting Optimization
Reduce meeting load by 20-30% through audit and restructuring. Focus on asynchronous communication for information sharing and reserve meetings for decision-making and complex discussions.
Focus Time Protection
Implement "focus time" blocks in team calendars. Elite teams maintain 4+ hours of continuous focus time daily, significantly impacting productivity scores.
Process Streamlining
Identify and eliminate bottlenecks in code review, deployment, and issue resolution processes. Target cycle time improvements of 15-25% through process optimization.
Tool Integration and Automation
Implement or optimize development tools, CI/CD pipelines, and automated testing to reduce manual overhead and improve deployment frequency.
Team Collaboration Patterns
Optimize cross-team collaboration through better communication channels, shared documentation, and structured knowledge transfer processes.
Sustainable Practices
Establish work-life balance policies and monitor workday intensity to ensure productivity improvements don't come at the cost of team burnout.
When implementing productivity measurement systems, privacy and compliance must be paramount. With 86% of employees believing it should be a legal requirement for employers to disclose monitoring tools, transparency is essential. (Worklytics)
Worklytics is built with privacy at its core, using data anonymization and aggregation to ensure compliance with GDPR, CCPA, and other data protection standards. (Worklytics) This approach allows organizations to gain valuable productivity insights while maintaining employee trust and regulatory compliance.
Best Practices for Ethical Productivity Measurement:
The future of engineering productivity measurement lies in more sophisticated, holistic approaches that balance multiple factors. Worklytics continues to innovate in this space, with new work modeling approaches that capture the complexity of modern software development. (Worklytics)
As AI tools become more prevalent in software development, productivity benchmarks will need to evolve. Early adopters are already seeing significant improvements in code generation and testing efficiency, but the full impact on industry benchmarks won't be clear until 2026.
As organizations mature in their hybrid work practices, we expect to see convergence in productivity patterns between remote and in-office teams. The key differentiator will be intentional design of collaboration and focus time rather than physical location.
The 2025 engineering productivity benchmarks provide unprecedented clarity on what constitutes competitive performance for software engineering teams. With LinearB's analysis of 6.1 million pull requests and Clockwise's focus-time insights from 80,000 engineers, we now have data-driven targets for cycle time, collaboration efficiency, and work sustainability. (LinearB)
The key insight from this comprehensive analysis is that elite productivity isn't about working longer hours or producing more code—it's about optimizing the entire system of work. Teams that achieve productivity scores above 85 consistently demonstrate fast cycle times (under 2.5 days), maintain adequate focus time (4+ hours daily), and practice efficient collaboration patterns.
Worklytics' approach to productivity measurement—combining technical output, collaboration efficiency, and work sustainability into weighted composite scores—provides a framework that engineering leaders can use to benchmark their teams and identify improvement opportunities. (Worklytics) The platform's privacy-first approach ensures that productivity insights can be gained while maintaining employee trust and regulatory compliance.
For engineering leaders looking to improve their team's productivity scores, the path forward is clear: start with comprehensive measurement, focus on system-level improvements rather than individual optimization, and maintain a sustainable approach that balances efficiency with team wellbeing. The 2025 benchmarks provide the roadmap—now it's time to execute.
Whether your team currently scores in the fair range (55-69) or is already performing at good levels (70-84), there are evidence-based strategies to reach elite performance. The combination of faster cycle times, protected focus time, and efficient collaboration patterns isn't just about hitting productivity targets—it's about creating an environment where engineers can do their best work while maintaining sustainable, fulfilling careers.
Based on LinearB's analysis of 6.1 million pull requests, elite software engineers achieve productivity scores in the 90th percentile range, good performers fall in the 70-89th percentile, and fair performers range from 50-69th percentile. These benchmarks span 20 metrics across the full Software Development Life Cycle including DevEx, DORA, and PM Hygiene metrics.
According to Jellyfish's research on 78,000 engineers, Innovation Allocation (time on new features) increased by 31% while Issue Cycle Time decreased by 23%. This indicates teams are becoming more efficient at delivery while dedicating more time to value-driving work that moves the business forward.
A competitive productivity score should include technical metrics like PR merge time, cycle time, and deploy frequency, plus social drivers like trust, autonomy, and psychological safety. Worklytics research shows that social drivers provide a fuller picture of team performance than velocity metrics alone, revealing important areas for improvement.
Modern benchmarks recognize that hybrid work has changed the workday structure, elongating the span but changing intensity patterns. Worklytics' workplace metrics benchmarks now include Workday Intensity (time on digital work as % of workday span) and Work-Life Balance metrics to better reflect remote and hybrid productivity realities.
Elite performers consistently operate in the top 10% across multiple dimensions including faster PR merge times, shorter cycle times, higher deploy frequency, and better code quality metrics. Good performers maintain solid execution in the 70-89th percentile range but may have opportunities for improvement in specific areas like PR size optimization or issue linking practices.
Teams should first establish baseline measurements across key metrics, then identify their current performance band (elite, good, or fair). Focus on improving 2-3 specific metrics at a time, such as reducing PR size, improving merge times, or increasing deploy frequency. Regular measurement and comparison against these 2025 benchmarks helps track progress and maintain competitive performance levels.
