Fast & Easy Implementation
Measure your productivity in 3 simple steps without disrupting your existing workflow
Benchmark Your Organization’s Software Engineering Performance
Analyze Your Performance
Based on Scientific Research
Our solution is built on the foundation of cutting-edge research at a leading university in the Bay Area
Transform Your Software Engineering Performance with Objective Metrics
Effortless Internal & Industry-wide Benchmarking
- Compare your organization's productivity against internal teams and industry peers
- Identify areas where your organization excels or needs improvement
- Gain insights to stay ahead of your competition
Objective Data for Strategic Decision-Making
- Use data-driven insights for decisions on insourcing vs. outsourcing, local vs. remote, and more
- Evaluate team and country performance with confidence
- Support investment decisions, such as adopting new tools or changing headcount
In-Depth Real-Time & Historical Analysis
- We use git history to show productivity metrics from the inception of your git repo
- Understand how past decisions impacted productivity and learn from historical trends
- Benefit from real-time analyses that keep you updated on your team's performance
Measuring True Output for Genuine Insights
- Move beyond competitors' easily gameable input or velocity-based metrics that don’t tell the whole story, such as story points, commit counts, or lines of code
- Uncover the true value of your software engineering efforts by focusing on output (through analyzing the actual source code)
Easy and Flexible Implementation
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Choose between:
- Granting access to your repos for scanning
- Using a docker image (for heightened security needs)
- Forego long implementation processes: our solution is plug & play
Support for 9+ Programming Languages
Coming soon
P10Y vs. Other Solutions: The Power of Measuring True Output for Unmatched Insights
P10Y: Measuring True Productivity
- Measure output by analyzing actual source code
- Hard to game productivity metrics
- Drives decision-making based on objective data
- Allow performance comparison across teams & industries
Other Solutions: Relying on Flawed Metrics
- Focuses on input or process, does not measure output
- Easily gameable metrics like story points or lines of code
- Uses subjective data and may lead to suboptimal outcomes
- Doesn’t allow objective and standardized performance comparison
How Existing (non-P10Y) Software Engineering Productivity Metrics May Create Counterproductive Incentives
| Metric | Definition | Counterproductive Incentive |
|---|---|---|
| Story Points (or Velocity Points) | Effort estimation for tasks | Encourages inflating the number of points a task will require to complete |
| Pull Request Counts | Number of proposed code changes | Encourages making smaller, less significant changes |
| Avg. Code Review Turnaround Time | Time for code review and approval | Encourages reviewers to be less diligent to improve their turnaround time |
| Release Frequency | Rate of software updates | Encourages prioritizing speed over quality Encourages feature bloat |
| Lines of Code | Number of lines written in a program | Encourages verbose, redundant code |
| Commit Counts | Number of updates to a code repository | Encourages multiple small commits instead of meaningful, larger updates |