opendrs

opendrs: PR blast radius analyzer and risk score for safe auto-approval

A calibrated 0-1 risk score for every pull request, so AI-reviewed, low-blast-radius PRs can be auto-approved into the merge queue while risky ones still wait for a human.

AI agents now write and review code faster than people can read it, so human review has become the bottleneck. Most pull requests are low risk, and a few cause most incidents. opendrs measures each PR's blast radius and risk: what the change touches, how many files depend on it, the history of the files it changes, author experience, missing tests and critical paths. It turns that into a 0-1 score using signals from your own git history. A team then sets a policy. For example, a PR that scores below 0.3 and has passed an AI review (Claude, CodeRabbit, Copilot or any check run) is approved and sent to the merge queue or auto-merge. Riskier PRs still wait for a human. The approach follows Meta's Diff Risk Score (DRS); opendrs is an independent, open-source implementation that runs in any git repository, with no service to host.

Contents: Install · Quick start · How it works · Features · Results · CLI and MCP · Documentation · Limitations · Compared with other tools · Prior art

Install

Website and guides: error-path.github.io/opendrs.

Quick start

Score your current branch against the default branch. No configuration or API key is needed:

npx @error-path/opendrs score

You get a score, a tier (green, yellow or red), the signals that drove it with their evidence, and the policy decision. --json prints the full result, and --fail-above 0.6 makes the command exit 1 above a threshold.

To score every pull request, add .github/workflows/opendrs.yml (or run npx @error-path/opendrs init):

name: opendrs
on:
  pull_request:
    types: [opened, synchronize, reopened, ready_for_review, labeled, unlabeled]
permissions:
  contents: read
  pull-requests: write
  checks: read
jobs:
  opendrs:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v5
        with:
          fetch-depth: 0   # required: the score reads past commits and authors
      - uses: error-path/opendrs@v0

This only comments and labels. Approval is off until you turn it on in .opendrs.yml on your default branch; see how to auto-approve low-risk PRs and the GitHub Action reference.

How it works

flowchart LR
  diff["git diff<br/>merge-base...head"] --> signals
  hist["git history"] --> signals
  graph["import graph"] --> signals
  llm["LLM judge<br/>(optional)"] --> signals
  signals["Signals<br/>each with evidence<br/>and provenance"] --> model["Logistic model<br/>score = sigmoid(b + Σ w·x)"]
  model --> tier["Tier<br/>green / yellow / red"]
  tier --> policy["Policy<br/>blocks, size cap, unknowns,<br/>AI review, sampling"]
  policy --> decision["auto-approve<br/>deferred-review<br/>waiting-ai-review<br/>human-review"]

1. Signals. Each signal provider reads the diff, the history and the import graph and emits features: size, how spread out the change is, past fixes and reverts in the touched files, the author's experience with them, complexity, blast radius (transitive dependents), critical paths and missing tests. Every signal records how it was obtained (observed, derived, inferred or unknown) and the evidence behind it. All signals
2. Model. A logistic regression turns the features into a probability. Each signal's contribution w·x is reported, so you can see why a PR scored what it did. Out of the box the weights are hand-set priors; opendrs calibrate fits them to your repository. Calibration and backtesting
3. Tiers. The score maps to green, yellow or red, mirroring Meta's gates. Thresholds can be absolute scores or percentiles of your own history.
4. Policy. Ordered rules turn the score into a decision. Policy and safety rails

Where opendrs sits in your PR flow

A PR now often gets two rounds of AI review before a human looks at it. Those rounds catch local defects: typos, missing null checks, an obvious off-by-one. By the time a human opens the PR, that work is done, and what is left is a design and architecture judgment call made with less context than before. opendrs is built for that moment: it tells the final human how much attention the PR needs and names up to five architecture and history facts — a new import between modules, a broken public contract, a file that usually changes with one the PR didn't touch — that neither the editor nor the AI review comments showed. See the captain brief.

Features

Every setting lives in .opendrs.yml, read from the base branch so a PR cannot loosen its own gate: configuration reference.

Results

CLI and MCP

Flags, exit codes, MCP tools and the agent skill: CLI, MCP server, Claude Code plugin and agent skill.

Documentation

Limitations

• The default weights are not fit to your code. Run backtest before trusting them, and calibrate when you have enough history.
• Labels are proxies for incidents, and small repositories give unstable numbers.
• The score is not a code review. It estimates the chance of an incident, not whether the change is correct.

The full list: known limitations.

Compared with other tools

The full comparison, with how each tool scores and where it runs, is at error-path.github.io/opendrs/alternatives.html.

Prior art and credits

• Meta's Diff Risk Score. Diff Risk Score: AI-driven risk-aware software development (Engineering at Meta, 2025) and Abreu et al., Moving Faster and Reducing Risk: Using LLMs in Release Deployment (2024). The feature set, the tiered gates and the capture-at-X %-gated metric come from this work. opendrs is not affiliated with Meta.
• Meta's RADAR. Adams et al., Automating Low-Risk Code Review at Meta (2026): the structured LLM reviewer with safe and risk categories, per-source policy (daily caps, track record), deferred review, the landing delay and the content blocklist follow this paper. Related Meta work behind hotspots and reviewer assignment: Mockus et al., "Metrics Driven Reengineering and Continuous Code Improvement at Meta" (ASE 2025); Paraspatki et al., "Code Reviewer Recommendation for High Risk Diffs at Scale" (FSE Companion 2026); Rigby et al., Improving Code Reviewer Recommendation (TOSEM 2025).
• Benchmarks and peers. Keshavarz and Nagappan, ApacheJIT (MSR 2022), the dataset bench/apachejit/ scores; Sayedsalehi, Rigby and Mockus, DRS-OSS (2025), the open-source LLM counterpart whose numbers the benchmark quotes.
• Just-in-time defect prediction and SZZ. Mockus and Weiss, "Predicting risk of software changes" (2000); Śliwerski, Zimmermann and Zeller, "When do changes induce fixes?" (2005); Kamei et al., "A large-scale empirical study of just-in-time quality assurance" (2013). The change-level features and the SZZ-style labelling follow this literature.
• REA (morluto/rea). opendrs adopts REA's evidence-first method: every conclusion is marked observed, derived, inferred or unknown, and unknown is never treated as safe. It also follows REA's layering of domain types, providers, a shared engine and thin adapters.

License

MIT