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Github Analytics

You:

Are there any meaningful GitHub insights that are similar to the DOR a metrics that we are talking about here or is this also bullshit? I wonder if there are some ghetto API’s and some AI and some data analytics that could show me who is committing code and how frequently they are, and if there’s an algorithm, I can create that measures theimpact of a commit or PR request. Based on code quality, change or other dynamics.

ChatGPT:

"GitHub metrics ain’t inherently bullshit—but what you do with them separates the rockstars from the roadies."

Alright, let’s unpack this. GitHub has a treasure trove of data that could offer meaningful insights if analyzed correctly. The key here is asking smarter questions than "who’s committing the most code?" (because that’s a fast track to incentivizing busy work). If you’re willing to go beyond surface-level metrics, there’s potential for some serious insights.

Meaningful GitHub Metrics (If Done Right)​

Here are a few GitHub insights that go beyond basic vanity metrics:

1. Contribution Analysis (Who’s Doing What?)​

  • What to Track:
    • Number of commits per contributor (normalized over time).
    • Size of changes (lines added/removed).
    • Files or modules most frequently touched by a contributor.
  • Why It Matters: Helps you identify team silos (e.g., one person "owns" a part of the codebase) or overloaded team members.
  • Danger: Focusing on commit count alone creates perverse incentives—encouraging small, meaningless commits for show.

2. Pull Request Activity (Collaboration Metrics)​

  • What to Track:
    • Time to open, review, and merge PRs.
    • Number of reviewers per PR.
    • Frequency of PRs marked as “changes requested.”
  • Why It Matters: Gives you a picture of team collaboration, responsiveness, and whether the review process is too bureaucratic or too lax.
  • Example Insight: Long review times or lots of requested changes could point to unclear requirements, poor code quality, or bottlenecks in your process.

3. Code Quality Impact​

This is where the real magic can happen if you pair GitHub data with AI.

  • What to Track:
    • Linting or static analysis results before and after a commit.
    • Test coverage and failure rates linked to specific commits or PRs.
    • Ratio of hotfix commits (fixes right after merges).
  • Why It Matters: Ties coding activity to quality outcomes, not just quantity. For example, a contributor with fewer commits might have the cleanest, most stable code.
  • AI Possibility: Use AI to predict the likelihood of a commit introducing bugs by analyzing patterns in file changes or PR history.

4. Ownership Dynamics​

  • What to Track:
    • "Bus factor": How many files/modules would become orphaned if a specific contributor left.
    • Churn rate: Frequency of changes to the same files or features over time.
  • Why It Matters: A high churn rate might indicate an unstable area of the codebase. Ownership insights show where you need better documentation or shared knowledge.

5. Impact of Commits/PRs​

Here’s where you can level up GitHub insights with algorithms and AI:

  • Potential Algorithm for Commit Impact:

    • Inputs:
      • Lines of code changed.
      • Files impacted (e.g., critical files vs. auxiliary files).
      • Dependency impact (does this change touch core modules or ripple across the system?).
      • Test coverage changes.
      • Production bugs reported after the commit.
    • Output: An "Impact Score" for each commit, showing how much it affects the system’s stability or complexity.

  • AI Application:

    • Train a model to analyze historical commits that led to bugs or outages and identify patterns (e.g., types of changes, coding style, etc.).
    • Use this model to score new PRs based on their likelihood of introducing problems.

APIs and Tools You Can Use​

GitHub already exposes a ton of useful data through its API, and you can enhance it with some third-party tools or custom scripts:

GitHub API​

  • What It Offers:
    • Commit history.
    • PR data (review times, comments, etc.).
    • Issues linked to PRs.
    • Code changes (lines added/removed).
  • How to Use It: Pair this data with custom analytics to generate meaningful dashboards or insights.

GitHub Advanced Security (Optional Extra)​

  • What It Offers: Scans for vulnerabilities, code smells, and security issues in commits.
  • How to Use It: Integrate security data into your commit impact analysis.

Code Analysis Tools (Add AI Magic)​

  • SonarQube: Integrate with GitHub to measure code quality for each commit.
  • OpenAI/GPT Models: Use a custom prompt to evaluate code diffs, e.g., “Summarize the purpose of this change and predict potential risks.”

The Real Challenge​

The hardest part isn’t collecting the data—it’s interpreting it in a way that leads to actionable insights. If you don’t tie metrics to real-world outcomes (e.g., fewer bugs, faster delivery, happier teams), it’s just more noise.

So, if you’re thinking about building your own analytics tool with some "ghetto APIs" and AI sprinkled in, here’s what you need:

  1. Start Simple: Build dashboards for commit/PR volume, review times, and churn rate.
  2. Introduce Context: Pull in external data, like test results or bug reports, to correlate metrics with outcomes.
  3. AI for the Win: Use AI to analyze patterns in commits or PRs and predict risks, inefficiencies, or areas needing improvement.

Let me know if you want a breakdown of how to architect this with GitHub APIs, TypeScript, and some badass data pipelines. We can turn this into something meaningful—not just another corporate report.

You:

Yes, please do the following as suggested, and please explain what you mean by ghetto API? Is this a reference to something I said along time ago or some other context?

…breakdown of how to architect this with GitHub APIs, TypeScript, and some badass data pipelines. We can turn this into something meaningful — not just another corporate report.

ChatGPT:

"Ghetto API" Explained​

First off, “ghetto API” was a phrase you used earlier in the conversation, and I ran with it. If it wasn’t clear or seemed off, I’ll clarify: it’s just a colorful way of describing something scrappy, lightweight, or maybe a bit duct-taped together to get the job done. It’s not about being polished or enterprise-grade but rather efficient and effective, even if it's cobbled together. Think of it as hustling with the tools you have to make something awesome—fast.

Architecture Breakdown: GitHub APIs, TypeScript, and Data Pipelines​

Let’s build this like a lean, mean analytics machine. Here’s how we can architect it:

Step 1: GitHub Data Collection​

We’ll use the GitHub REST API to pull in all the juicy data for contributors, commits, pull requests, reviews, and code changes.

  • Tech: TypeScript + Axios (for API calls)
Key API Endpoints​
  1. Commits:
    • URL: GET /repos/{owner}/{repo}/commits
    • Use this to fetch all commits for a repo, including author, timestamp, and message.
  2. Pull Requests:
    • URL: GET /repos/{owner}/{repo}/pulls
    • Includes details about who opened the PR, review comments, merge time, etc.
  3. Code Changes:
    • URL: GET /repos/{owner}/{repo}/pulls/{pull_number}/files
    • Lists files changed, lines added/removed, and impact area.
  4. Issues Linked to Commits/PRs:
    • URL: GET /repos/{owner}/{repo}/issues
    • To see if changes were tied to bug fixes or feature requests.
Example Code Snippet​
import axios from 'axios';

const GITHUB_API_BASE = 'https://api.github.com';
const GITHUB_TOKEN = 'your-github-token'; // Use a personal access token here

async function fetchCommits(owner: string, repo: string) {
  const response = await axios.get(`${GITHUB_API_BASE}/repos/${owner}/${repo}/commits`, {
    headers: { Authorization: `Bearer ${GITHUB_TOKEN}` },
  });
  return response.data;
}

async function fetchPullRequests(owner: string, repo: string) {
  const response = await axios.get(`${GITHUB_API_BASE}/repos/${owner}/${repo}/pulls`, {
    headers: { Authorization: `Bearer ${GITHUB_TOKEN}` },
    params: { state: 'all' },
  });
  return response.data;
}

(async () => {
  const commits = await fetchCommits('your-org', 'your-repo');
  console.log('Commits:', commits);

  const pullRequests = await fetchPullRequests('your-org', 'your-repo');
  console.log('Pull Requests:', pullRequests);
})();

Step 2: Data Pipeline for Analytics​

We’ll process the raw GitHub data into meaningful metrics using Node.js + a database + an analytics engine.

Pipeline Steps​

  1. Ingest Data:

    • Use the GitHub API to fetch data on a schedule (e.g., every 6 hours).
    • Store raw data in a database (e.g., PostgreSQL).

  2. Transform Data:

    • Normalize commit and PR data into metrics like:
      • Total commits per author.
      • Average time to merge PRs.
      • Lines added/removed.
      • Areas of the codebase touched.

  3. Analyze and Enhance:

    • Calculate impact scores based on:
      • Code churn (frequent changes to the same files).
      • Test coverage changes.
      • Dependency impact (core vs. auxiliary files).
    • Use AI models to predict bug likelihood or risky changes.
Example Database Schema​
CREATE TABLE commits (
  id SERIAL PRIMARY KEY,
  sha TEXT NOT NULL,
  author TEXT,
  message TEXT,
  timestamp TIMESTAMP,
  lines_added INT,
  lines_removed INT,
  files_changed INT
);

CREATE TABLE pull_requests (
  id SERIAL PRIMARY KEY,
  pr_number INT NOT NULL,
  author TEXT,
  state TEXT,
  created_at TIMESTAMP,
  merged_at TIMESTAMP,
  review_comments INT,
  changes_requested INT
);

CREATE TABLE analytics (
  id SERIAL PRIMARY KEY,
  author TEXT,
  commits INT,
  lines_changed INT,
  average_merge_time INTERVAL,
  change_failure_rate FLOAT
);

Step 3: Visualize Metrics​

The final step is presenting insights in a way that’s actionable and not just pretty charts.

  • Frontend: Use a lightweight dashboard framework like React or Angular.
  • Charting Library: Integrate Chart.js or D3.js for data visualization.
Example Metrics Dashboard​
  • Top Contributors: Ranked by impact (not just commit count).
  • High-Churn Files: Flag areas with frequent, large changes.
  • Review Bottlenecks: PRs with long review/merge times.
  • Quality Indicators: Test coverage changes, bugs post-deployment.

Step 4: AI-Powered Impact Scoring​

Here’s where you flex the big brain energy and bring in AI.

Steps​

  1. Train a Model:

    • Use historical GitHub data to train a model to predict:
      • Bug likelihood based on commit size/type.
      • Areas of the codebase likely to break with certain changes.
    • Tools: OpenAI GPT for text/code analysis, or a local AI model like Hugging Face Transformers.

  2. Run Predictions:

    • For each PR/commit, calculate:
      • Risk score (likelihood of introducing bugs).
      • Dependency ripple effect (does it touch core modules?).
Example Scoring Algorithm​
function calculateImpactScore(linesChanged: number, filesChanged: number, testCoverageDelta: number): number {
  const churnPenalty = filesChanged > 5 ? 10 : 0;
  const coverageBonus = testCoverageDelta > 0 ? 5 : -5;
  return linesChanged * 0.1 + churnPenalty + coverageBonus;
}

Why This Isn’t Just Another "Corporate Report"​

  • Actionable Insights: This isn’t about showing flashy charts; it’s about identifying real bottlenecks, risky changes, and high-impact contributors.
  • AI as a Force Multiplier: Analyzing code quality and predicting issues moves beyond counting commits.
  • Empowers Developers: Metrics help teams, not just managers, by flagging problem areas and improving workflows.

Let me know if you want me to dive deeper into specific parts, like the AI side, database setup, or visualizations!