OSS_Dev_Analytics

Helping open-source contributors understand the health and efficiency of their communities through data-driven insights.

Overview

OSS_Dev_Analytics provides a centralized dashboard for the Open Source with SLU community. We transform raw GitHub activity into actionable metrics, helping maintainers and contributors identify bottlenecks and celebrate progress.

Tech Stack

• Backend: Python 3.x, Pandas (Data processing), PyGithub (GitHub API Wrapper).
• Frontend: React.js, Vite, Tailwind CSS.
• Data Pipeline: Sprint-based filtering and JSON-structured analytics.

Project Architecture

• Data Acquisition: Python scripts fetch data from GitHub via the REST API.
• Sprint Filtering: Activity is categorized into predefined two-week sprints.
• Preprocessing: Pandas is used to calculate "Time to Merge," "Lead Time," and "Velocity."
• Visualization: A React + Vite frontend consumes the processed JSON to render interactive charts.

Dynamic Health Score Calculation

We implemented a dynamic health scoring system that adapts to each repository's selected metrics instead of using a fixed formula

How it works

• Each repository defines a set of metrics to track
• Only those selected metrics are used in the calculation
• Metric values are normalized (0-100 scale)
• A weighted average is computed based on selected metrics

Formula

Health Score = Σ(metric × weight) / Σ(weights)

Example

If a repository selects:

• issue_resolution = 80
• commit_volume = 60 Then: Health Score = (80×0.25 + 60×0.20) / (0.25 + 0.20)

Edge Cases

• If no metrics are selected -> an error is returned
• If some metric values are missing -> they are ignored
• If no valid data is available -> an error is returned

Note

Currently, metric selection is simulated using a temporary in-memory configuration (fake database). This will later be replaced with database/Okta-based user configurations.

🤝 Getting Started

Are you interested in contributing to our organization-wide analytics?

1. Check out our Onboarding Document.
2. Join the conversation in the #oss-dev-analytics Slack channel.
3. Look for "Good First Issues" in our repository!

Communication:

All communication is held via the

OSS_Dev_Analytics Slack group chat: https://oss-slu.slack.com/archives/C09C1AQ181L

and

OS SLU Slack: