djaodjin/djaodjin-saas
Django application for software-as-service and subscription businesses
Experimentation Intensity Score
34.5 / 100
A single, comparable figure that combines engagement, regularity, issue refinement and integration into one view of a repository's development activity — calculated fresh relative to every other repository currently in the database, so scores stay meaningful as more repositories are added.
How is this calculated, and why did I get this score?
- Why you got this score
- This score of 34.5/100 is the average of 4 applicable dimensions: Engagement (0.09), Regularity (0.57), Issue Refinement (0.50), Integration (0.21).
- How it's calculated
- Each dimension is min–max normalised against every repository currently in the database (reverse-coded where a lower raw value is the stronger pattern), then the applicable dimensions are averaged and scaled to 0–100.
- What it represents
- One comparable figure for how much iterative, structured development activity a repository shows — not code quality or project success.
- How to interpret it
- It's relative to whatever repositories are currently stored, so treat it as a within-this-dataset ranking aid, not a universal benchmark.
- How to use it
- Compare repositories side by side, and check the four dimension scores and metric cards below for the evidence behind the number. Full explanation →
Commit activity
Feeds into: Engagement & Regularity
- Commit frequency
- 1.32 / week
- Active-day ratio
- 11.1%
- Commit interval variability
- 7.2 days
- Longest inactivity gap
- 26.9 days
Issue activity
Feeds into: Issue Refinement
- Total issues
- 1
- Issue closure rate
- 0.0%
- Mean comments per issue
- 2.00
- Mean issue resolution time
- N/A
Pull request activity
Feeds into: Integration
- Total pull requests
- 6
- Merge rate
- 16.7%
- Reviewed PR ratio
- 16.7%
- Mean PR cycle time
- 38.5 days
Commit activity over time
Learning Quality Indicator
50.0%
Share of classified repository text — commit messages, issues, pull requests and reviews — that reads as substantive learning-oriented language (problem identification, experimentation, reflection or refinement) rather than routine text.
How is this calculated, and why did I get this score?
- Why you got this score
- Of the 44 pieces of repository text analysed, 22 (50.0%) were classified as substantive learning-oriented language, most often 'refinement' (17); the remaining 22 were classified as routine text.
- How it's calculated
- Every classified text snippet is assigned one of five categories by a TF-IDF + classifier: Problem Identification, Experimentation, Reflection, Refinement, or None. The indicator is the percentage that fell into one of the four substantive categories.
- What it represents
- An estimate of how much repository text reflects learning-oriented language vs. routine/administrative text.
- How to interpret it
- The current classifier (Linear SVC) was trained on 100 labelled examples and measured 49.6% accuracy in cross-validation. Treat classifications as exploratory suggestions, not certainties.
- How to use it
- As a starting point for which artefacts might be worth reading directly — not proof that learning occurred. Full explanation →
Problem Identification
7% (3)
Experimentation
0% (0)
Reflection
5% (2)
Refinement
39% (17)
None
50% (22)