Feature request
Description the feature
Develop the core Python data module responsible for interacting with the GitHub API to extract raw repository metrics regarding issues, Pull Requests (PRs), commits, and contributor profiles.
Goals of this feature:
- Efficient Data Extraction: Fetch historical data from GitHub API endpoints (including issue creation dates, labels, assignment status, response times, and merge dates).
- Rate Limit Management: Implement a smart caching strategy to prevent hitting GitHub's strict API rate limits for unauthenticated or basic authenticated requests.
- Metric Calculators: Write backend helper functions to calculate core community health indicators like Time-to-First-Response (TTFR) and Time-to-Merge (TTM).
Is your feature request related to a problem?
Yes. The GitHub API imposes strict hourly request thresholds (Rate Limits). Without an optimized data access layer and aggressive caching, the dashboard will lag, timeout, or stop working entirely if multiple users browse it simultaneously.
Do you have any suggestions on how to add this feature in scanapi ?
- Tools: Python
requests, PyGithub, or httpx, combined with Streamlit's built-in memory/disk caching mechanisms (@st.cache_data).
- Approach: Build a dedicated
github_client.py utility file that parses API responses and returns clean, structured Pandas DataFrames ready for the visualization components.
Feature request
Description the feature
Develop the core Python data module responsible for interacting with the GitHub API to extract raw repository metrics regarding issues, Pull Requests (PRs), commits, and contributor profiles.
Goals of this feature:
Is your feature request related to a problem?
Yes. The GitHub API imposes strict hourly request thresholds (Rate Limits). Without an optimized data access layer and aggressive caching, the dashboard will lag, timeout, or stop working entirely if multiple users browse it simultaneously.
Do you have any suggestions on how to add this feature in scanapi ?
requests,PyGithub, orhttpx, combined with Streamlit's built-in memory/disk caching mechanisms (@st.cache_data).github_client.pyutility file that parses API responses and returns clean, structured Pandas DataFrames ready for the visualization components.