ReferenceScoring Formulas

Scoring Formulas

The analyzer uses explicit formulas so repository scores are explainable instead of opaque.

Activity Score

Activity measures how active a repository is across five signals.

activityScore=(0.40×commitNorm)+(0.25×starsNorm)+(0.15×forksNorm)+(0.10×contributorsNorm)+(0.10×issuesNorm)\mathrm{activityScore} = (0.40 \times \mathrm{commitNorm}) + (0.25 \times \mathrm{starsNorm}) + (0.15 \times \mathrm{forksNorm}) + (0.10 \times \mathrm{contributorsNorm}) + (0.10 \times \mathrm{issuesNorm})
MetricNormalizationCapReasoning
Commits in last 30 daysLinear500About 16 commits per day already represents extreme velocity
StarsLogarithmic200000Covers the vast majority of public repositories
ForksLogarithmic50000Fork distribution is heavily skewed
ContributorsLogarithmic10001000+ contributors is exceptional
Open issuesLogarithmic10000Extremely high but still bounded

Normalization Functions

logNormalize(v,c)=min⁡(log⁡10(v+1)log⁡10(c+1),1.0)\mathrm{logNormalize}(v, c) = \min\left(\frac{\log_{10}(v + 1)}{\log_{10}(c + 1)}, 1.0\right)linearNormalize(v,c)=min⁡(vc,1.0)\mathrm{linearNormalize}(v, c) = \min\left(\frac{v}{c}, 1.0\right)

Why Logarithmic Scaling Is Necessary

GitHub popularity metrics follow a power-law distribution. Without log scaling, small and medium repositories collapse toward zero compared with projects like React or Linux.

5230000≈0.0000217=0.002%\frac{5}{230000} \approx 0.0000217 = 0.002\%log⁡10(6)log⁡10(230001)≈0.145=14.5%\frac{\log_{10}(6)}{\log_{10}(230001)} \approx 0.145 = 14.5\%

That second value is why logarithmic scaling produces a more useful comparison space.

Complexity Score

Complexity estimates the cost of understanding and maintaining the codebase.

complexityScore=(30×fileCountNorm)+(30×langDiversityNorm)+(20×hasDependencyFile)+(20×depCountNorm)\mathrm{complexityScore} = (30 \times \mathrm{fileCountNorm}) + (30 \times \mathrm{langDiversityNorm}) + (20 \times \mathrm{hasDependencyFile}) + (20 \times \mathrm{depCountNorm})
ComponentMax pointsMethodImplementation detail
File count30Log scale, cap 50000Uses the Git Tree API with recursive=1
Language diversity30Linear, cap 15Count of distinct languages from the Languages API
Has dependency file20BinaryDetects common manifest files across ecosystems
Dependency count20Linear, cap 200Exact for Node.js, estimated for non-JS repositories

Learning Difficulty

The final difficulty score is the midpoint of activity and complexity.

combined=activityScore+complexityScore2\mathrm{combined} = \frac{\mathrm{activityScore} + \mathrm{complexityScore}}{2}
Combined scoreLevelDescription
< 35BeginnerGood for newcomers
35-65IntermediateSome experience needed
> 65AdvancedSignificant experience required

Practical Interpretation

  • High activity and low complexity usually indicates a welcoming, actively maintained project.
  • Low activity and high complexity can indicate a difficult repository with a slower learning path.
  • High activity and high complexity often marks important but demanding repositories.

Related

ArchitectureOperations & Edge Cases