ai-code-quality-auditor 0.2.0

Empirical Safety Harness for agentic AI coding systems. Scores AI-generated code on 5 metrics across 5 vendor conditions against one fixed spec.

AI Code Quality Auditor — the Referee Tool

An empirical Safety Harness for agentic AI coding systems. Quantifies where AI-assisted development fails at governance, security, and ethical alignment — before the code reaches production.

🟢 Try it in 30 seconds:

pipx install ai-code-quality-auditor
auditor --help

🚀 Or wire it into your CI in 6 lines (.github/workflows/auditor.yml):

jobs:
  audit:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: dominicrume/NEW-enterprise-ai-code-quality-auditor@main
        with:
          run-id: ${{ github.run_id }}
          conditions: claude_code,cursor_agent

📊 Live dashboard: https://auditor-dashboard.fly.dev (pending deploy — see below)

This is the experimental instrument for the MSc dissertation "AI-Assisted Coding Assessment Tool: Evaluating LLM Performance, Governance, and Security in an Agent Education System" (Aston University, MSc AI & Business Strategy). The same instrument is the working prototype for the PhD extension at the Aston-Capgemini Centre of Excellence for Enterprise AI.

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What it does

Given a fixed specification (the "spec box"), the Auditor:

1. Runs five experimental conditions against the same task (human control, visualisation→Claude→Replit, Cursor IDE, autonomous agent).
2. Captures every output and every interaction event.
3. Scores each result on five empirical metrics: security vulnerability density, cyclomatic complexity, code duplication, hallucination frequency (features outside spec), and keystroke dynamics (correction frequency).
4. Emits CSV/JSON reports for statistical comparison.

Quick start

cp .env.example .env
pip install -e .
auditor run --spec specs/agent_education_system.yaml --workflow human_control
auditor report --out data/reports/

Read in this order

1. docs/ARCHITECTURE.md — how the pieces fit
2. docs/METHODOLOGY.md — how an experiment is run
3. docs/METRICS.md — what each metric means and how it's computed
4. docs/ETHICS.md — GDPR, synthetic data, academic integrity
5. docs/DISSERTATION_LINKAGE.md — which folder serves which proposal section
6. docs/ROADMAP.md — the PhD extension (API security + enterprise risk)

Principles

• One analyzer per metric. One adapter per AI workflow. Single responsibility.
• The spec is data, not code — externalised in specs/ for reproducibility.
• Synthetic data only. No PII, no proprietary corporate records, ever.
• Every analyzer has a test. Green tests = trustable experiment.