qualbench 0.3.2

CI for AI-generated code — measures production readiness, not just correctness

QualBench — CI for AI-Generated Code

AI Cost Tracking

This project uses AI-generated code. Total cost: $3.1500 with 21 AI commits.

Generated on 2026-06-29 using openrouter/deep/deep-v4-pro

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Correct code is not the same as mergeable code. eslint + code review, but for AI. Add to your pipeline in 2 minutes.

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60 seconds to your first score

pip install qualbench
qualbench quickstart

No config, no API keys. QualBench evaluates your current diff and prints a Quality Score.

Add to CI in 2 minutes

# .github/workflows/qualbench.yml
name: QualBench
on: [pull_request]
jobs:
  quality-check:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: semcod/qualbench-action@v1
        with:
          tool: prollama
          fail_on_score: 70

Every AI-generated PR gets a quality review comment. Set fail_on_score and the pipeline fails if quality is below your threshold.

🧠 QualBench Review

Quality Score: 78/100

  ❌ Complexity increased (+12%)
  ⚠ Security: 1 new medium-severity finding
  ✔ Tests pass, no regressions

Verdict: needs_review

CI/CD Examples

GitHub Action (recommended)

# .github/workflows/qualbench.yml
name: QualBench
on: [pull_request]
jobs:
  quality-check:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: semcod/qualbench-action@v1
        with:
          tool: prollama
          fail_on_score: 70

GitLab CI

# .gitlab-ci.yml
qualbench:
  stage: test
  image: python:3.12-slim
  before_script:
    - pip install qualbench
  script:
    - qualbench run --tool prollama --json --fail-on-score 70
  only:
    - merge_requests

Azure DevOps

# azure-pipelines.yml
steps:
  - task: UsePythonVersion@0
    inputs:
      versionSpec: '3.12'
  - script: |
      pip install qualbench
      qualbench run --tool prollama --json --fail-on-score 70
    displayName: 'QualBench Quality Check'

Jenkins

// Jenkinsfile
stage('Quality Check') {
    steps {
        sh '''
            pip install qualbench
            qualbench run --tool prollama --fail-on-score 70
        '''
    }
}

CircleCI

# .circleci/config.yml
version: 2.1
jobs:
  quality:
    docker:
      - image: python:3.12-slim
    steps:
      - checkout
      - run: pip install qualbench
      - run: qualbench run --tool prollama --fail-on-score 70
workflows:
  quality-check:
    jobs:
      - quality

The problem

AI coding tools resolve 70–80% of benchmark tasks. But most AI-generated PRs are not mergeable without human fixes. Every existing benchmark asks "do tests pass?" — nobody asks "would a senior developer approve this PR?"

Six dimensions of production readiness

Dimension	What it measures	Weight
Correctness	All tests pass, no regressions	25%
Mergeability	Would a senior dev merge this? (1–5)	25%
Security	New vulnerabilities introduced	15%
Code quality	Complexity delta, dead code	15%
Iterations	Attempts to reach acceptable output	10%
Cost efficiency	USD per successful patch	10%

Verdicts: ready_to_merge (≥85), needs_review (65–84), not_merge_ready (<65).

CLI

qualbench run --tool prollama          # score current diff
qualbench run --tool prollama --json   # portable JSON output
qualbench run --mode cheap             # lowest-cost models
qualbench quickstart                   # first score in 60 seconds
qualbench compare my_tool              # vs leaderboard
qualbench info                         # dataset summary
qualbench doctor                       # check dependencies

One portable format everywhere

CLI, API, GitHub Action — same JSON schema. See docs/schema.md.

Adding your tool

cp runners/template.py runners/my_tool.py
# Implement run() → return portable schema
qualbench run --tool my_tool
# Submit PR with results

License

Licensed under Apache-2.0.

Status

Last updated by taskill at 2026-04-25 13:46 UTC

Metric	Value
HEAD	c199cf4
Coverage	—
Failing tests	—
Commits in last cycle	26

Repository received a mix of fixes, refactors and configuration updates: tests were hardened (mocks added), a JSON test fix was applied, many vallm/style issues and magic numbers were addressed, and release-related features (v0.3.0, Supervisor AI, new runners) were added. PyQual configuration thresholds and gates were adjusted and documentation/TODOs were updated after a PyQual run.