driftlens-ai 0.1.0

Code quality drift monitor for AI-generated code

DriftLens

AI coding agents make you faster. DriftLens makes sure they don't make your code worse.

AI agents write code at superhuman speed — but SlopCodeBench (arXiv:2603.24755) shows they produce 2.2x more verbose code than humans, and code quality degrades in 80% of agentic trajectories. DriftLens catches this drift before it ships: baseline your codebase, then score every change against it.

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Quick start (30 seconds)

pip install driftlens
cd your-project
driftlens init        # snapshot current state as baseline
driftlens report      # score drift since baseline

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What it measures

DriftLens tracks four structural signals, all computed from the AST — no regex, no LLM calls.

Metric	What it captures
Verbosity ratio	Lines per logical operation — AI agents pad code with redundant assignments, dead branches, and over-commented obvious logic
Structural similarity	Cosine distance between AST fingerprints — catches wholesale rewrites that preserve behavior but destroy architecture
Complexity concentration	Gini coefficient over cyclomatic complexity — flags when complexity pools into a few giant functions instead of staying distributed
Naming consistency	Identifier entropy and length distribution — detects drift toward generic names (data, result, tmp) or absurdly verbose ones

These four signals are combined into a single composite drift score (0–100). Higher = more drift from your baseline.

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CLI commands

Command	Description
driftlens init	Snapshot the current codebase as the baseline
driftlens score	Print the composite drift score (0–100)
driftlens report	Full breakdown — per-metric scores with Rich table output
driftlens ci	Exit 1 if drift score exceeds threshold (for CI gates)

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CI/CD integration

Drop this into any GitHub Actions workflow to gate PRs on drift:

- uses: Su760/driftlens@main
  with:
    threshold: "40"

DriftLens will fail the job if the composite drift score exceeds the threshold, printing a breakdown so you know exactly which metric regressed.

Full workflow example:

name: Drift check
on: [pull_request]

jobs:
  drift:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
        with:
          fetch-depth: 0
      - uses: Su760/driftlens@main
        with:
          threshold: "40"

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Severity levels

Score	Severity	Meaning
0–15	Healthy	Code is consistent with baseline
16–35	Mild	Minor drift; worth a second look
36–60	Significant	Structural changes detected; review carefully
61–100	Critical	Major drift; likely AI-introduced degradation

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How it works

Step 1 — Baseline. driftlens init walks your source tree, computes the four metrics for every Python file, and saves a JSON snapshot in .driftlens/baseline.json. This snapshot is your ground truth.

Step 2 — Drift detection. On every subsequent run, DriftLens recomputes the same metrics and compares them against the snapshot. Each metric returns a normalized delta (0–1), where 0 means identical and 1 means maximum measurable deviation.

Step 3 — Scoring. The four deltas are combined into a composite score using fixed weights:

score = 0.30 × verbosity + 0.30 × structure + 0.25 × complexity + 0.15 × naming

The result is scaled to 0–100. Verbosity and structure carry the most weight because they are the primary failure modes documented in SlopCodeBench.

All metrics are AST-based. DriftLens never calls an LLM, never sends your code anywhere, and produces deterministic results. A CI run that passes locally will pass remotely.

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Contributing

Pull requests are welcome. Before opening one:

1. Run pytest tests/ -v — all tests must pass
2. Do not modify files in tests/fixtures/ — they are calibrated ground truth
3. New metrics follow the same pattern: analyze_* → compute_* → compare_*

See CONTRIBUTING.md for more detail.

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License

MIT — see LICENSE.