AI Slop Detector v2.6.2: Integration Test Evidence (because “green CI” can still be hollow)

[flamehaven01]

What is “AI Slop”?

AI Slop is code that looks legitimate but carries little causal weight.

• It’s not “broken.”
• It’s not “malicious.”
• It’s just convincingly empty.

It often shows up as:

• promises outrunning evidence (“production-ready”, “scalable”)
• tests that exist, but don’t hit real dependencies
• structure and documentation growing faster than implementation

---

Community Feedback (and why this release exists)

This release exists because of a thoughtful comment from OnlineProxy (https://onlineproxy.io/).

They described a “complete-looking” repo with green CI that still felt hollow—and pointed out the real red flag:

CI is green, but 0 integration tests hit real dependencies.

That’s not a nitpick. It’s a real production failure mode.

So I treated that feedback like a bug report—and shipped v2.6.2 as the patch.

---

What’s new in v2.6.2

1) Integration Test Evidence (explicit split)

“Tests exist” isn’t enough.
v2.6.2 distinguishes:

• tests_unit (fast, isolated)
• tests_integration (hits real dependencies / realistic boundaries)

Detection uses four layers:

1. Path-based (tests/integration/, e2e/, it/)
2. Filename patterns (test_integration_*.py, *_integration_test.py)
3. Pytest markers (@pytest.mark.integration, @pytest.mark.e2e)
4. Runtime signals (TestClient, testcontainers, docker-compose)

---

2) Claims now require integration evidence

Strong claims now require stronger proof:

• production-ready → requires tests_unit + tests_integration
• scalable / fault-tolerant → requires tests_integration

This closes the gap: code that looks complete, but proves nothing under real dependencies.

---

3) Clearer reports & questions

The goal isn’t “more numbers.” It’s more inspectable output.

Reports and questions now surface:

• unit vs integration test breakdown
• explicit warnings when integration tests are missing but production claims exist
• more readable evidence labels (e.g., “integration tests”)

---

Quick start

# Install / upgrade
pip install -U ai-slop-detector

# Scan a project
slop-detector --project .

CI examples

# Soft: report only (never fails)
slop-detector --project . --ci-mode soft --ci-report

# Hard: fail on thresholds
slop-detector --project . --ci-mode hard --ci-report

# Claims strict: fail when production claims lack integration-test evidence
slop-detector --project . --ci-mode hard --ci-report --ci-claims-strict

---

Why this matters (in one line)

AI-era failures often aren’t syntax failures.
They’re verification gaps hidden behind clean structure and green CI.

v2.6.2 makes one of the most common gaps measurable:

“0 integration tests” is now something you can detect, report, and gate.

---

Links

• Repo: https://github.com/flamehaven01/AI-SLOP-Detector
• CI: https://github.com/flamehaven01/AI-SLOP-Detector/actions
• Changelog: https://github.com/flamehaven01/AI-SLOP-Detector/releases/tag/v2.6.2

---

We’re in an era where a “tiny” suggestion can turn into real tooling.
Your feedback doesn’t just help one maintainer—it can become reusable engineering that helps the next AI developer ship with more proof and less guesswork.