Linux Foundation Data Analysis

The Open Source
Data Adventure

An interactive journey through the hidden patterns within the LFX Leaderboards dataset. Exploring millions of lines of code and contribution metrics across the world's largest open source ecosystem.

Quest 01

The Efficiency Paradox

Do you need a massive team to move fast?

Methodology
Commits per Contributor Analysis

We calculate Commits รท Active Contributors to measure individual productivity. A high ratio indicates a small, highly focused team. A low ratio suggests distributed effort across many contributors.

Top 10 Most Efficient Projects

๐Ÿ’ก
Key Discovery
CBT Tape achieves an extraordinary 1,138 commits per contributor โ€” with only 3 people making 3,414 commits total. Small teams CAN deliver massive output when focused.
1 CBT Tape
2 Mushroom Observer
3 SmokeDetector
Quest 02

The Triage Trap

If a project responds instantly, do they fix bugs faster too?

Hypothesis
Response Time vs Resolution Rate

We compare First Response Time (how quickly issues get acknowledged) with Resolution Rate (percentage of issues actually closed). Intuition says fast responders should be fast fixers... but is that true?

Response Time vs Resolution Rate

Projects
โš ๏ธ
Surprising Finding
The correlation between response time and resolution rate is only 0.03 That's essentially zero correlation. Fast acknowledgment bots don't translate to faster fixes. Speed of response โ‰  quality of resolution.
Takeaway
Don't Judge by First Response

Many projects use automated bots that respond instantly with "Thanks for your issue!" but this metric is meaningless for predicting actual problem resolution. Look at resolution rate and time to close instead.

Quest 03

Building vs Painting

Are they building a skyscraper or just repainting the walls?

Analysis
Commit Activity vs Codebase Growth

High commits + growing codebase = Active development
High commits + stable codebase = Maintenance mode
This reveals whether projects are expanding features or polishing existing code.

Activity vs Codebase Size

Log Scale
--
High Maintenance Projects
--
Rapid Growth Projects
Quest 04

Hidden Gems

Which small projects have disproportionate corporate backing?

Formula
Organizational Diversity Ratio

Organizations รท Contributors reveals projects where many companies are invested but few people contribute. These are often stable, critical infrastructure that enterprises depend on โ€” perfect for adoption.

Organizations vs Contributors

๐Ÿ’Ž
Hidden Gems Discovered
These projects are trusted by many organizations but maintained by focused teams โ€” ideal candidates for enterprise adoption.
Quest 05

The Bus Factor Watchlist

Which tiny teams are doing massive work?

Risk Analysis
Small Team, Big Output = High Risk

Projects with โ‰ค50 contributors generating thousands of commits are impressive but risky. If key maintainers leave, the project could stall. These are dependencies you should monitor closely.

Small Teams, Massive Output

๐Ÿšจ
High Risk Projects
These projects have exceptional output but concentrated ownership. Consider contributing to help distribute the maintenance burden.
Quest 06

Burnout Signals

Who's running out of steam?

Warning Signs
High Productivity + Declining Momentum

Projects that were highly productive but show declining momentum may indicate maintainer burnout, key contributors leaving, or funding issues. These need community support.

Productivity vs Momentum

Declining
Growing
๐Ÿ”ฅ
Projects Losing Steam
Quest 07

Libraries vs Applications

Is a small contributor count always a bad sign?

Context Matters
Project Type Changes the Interpretation

Libraries: High corporate use + small team = Healthy! Stable APIs don't need constant changes.
Applications: High corporate use + small team = Warning! Companies using but not contributing back.

Project Type Distribution

--
Healthy Libraries
--
Apps to Watch
Quest 08

The Churn Trap

Are they building new features or rewriting the same code forever?

Formula
Churn Ratio = Commits รท Net Code Change

Low ratio (~1): Every commit adds lasting value. The project is growing.
High ratio (>100): Hundreds of commits for tiny net changes. Activity without progress.

Activity vs Net Growth

High Churn
Healthy
๐Ÿ”„
High Churn Projects
These projects have high activity but low net growth โ€” could indicate stabilization, refactoring, or technical debt cleanup.
Conclusion

Key Takeaways

01
Speed โ‰  Solutions
Fast response times don't correlate with resolution rates. Don't be fooled by automated acknowledgments.
02
Watch for Burnout
Islet and CheriBSD show declining momentum. If you depend on them, consider contributing.
03
Respect the Libraries
Small contributor counts aren't always bad. Stable libraries like MarkupSafe are pillars of the ecosystem.
04
Question Activity
High churn might mean stabilization โ€” or it might mean spinning wheels. Context matters.
โœจ
The Grand Insight
"Data doesn't lie, but it does whisper. You just have to listen closely."