Apply minimum-sample floor to weekly AI-adoption % (fixes #189)

iris/analysis/activity_timeline.py

@@ -147,11 +147,16 @@ def calculate_activity_timeline(
             classified = classify_commit(c)
             intent_dist[classified.intent.value] += 1
 
-        # Origin distribution
-        origin_dist: dict[str, int] = defaultdict(int)
-        for c in wc:
-            origin = classify_origin(c)
-            origin_dist[origin.value] += 1
+        # Origin distribution (only meaningful with enough commits — same
+        # floor as stabilization; a single AI-tagged commit in an otherwise
+        # quiet week shouldn't be able to swing weekly AI-adoption to 100%).
+        origin_dist: dict[str, int] = {}
+        if total_commits >= MIN_COMMITS_FOR_RATIO:
+            origin_counts: dict[str, int] = defaultdict(int)
+            for c in wc:
+                origin = classify_origin(c)
+                origin_counts[origin.value] += 1
+            origin_dist = dict(origin_counts)
 
         # Stabilization and churn (only meaningful with enough commits)
         stab_ratio = None

tests/test_activity_timeline.py

@@ -0,0 +1,82 @@
+"""Tests for activity_timeline's weekly origin-distribution sample floor (#189).
+
+A week's origin_distribution (which drives weekly AI-adoption %) previously
+had no minimum commit count, unlike stabilization_ratio — a single AI-tagged
+commit in an otherwise quiet week could swing that week to 100% AI adoption,
+a statistically meaningless spike that leaked into the org timeline chart as
+visible noise.
+
+Runnable as: `python -m pytest tests/test_activity_timeline.py -v`
+"""
+
+import sys
+from datetime import datetime, timezone
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
+
+from iris.analysis.activity_timeline import (
+    MIN_COMMITS_FOR_RATIO,
+    calculate_activity_timeline,
+)
+from iris.models.commit import Commit
+
+
+def _commit(day: int, attribution_trailers: list[str] | None = None) -> Commit:
+    return Commit(
+        hash=f"h{day}",
+        author="Alice",
+        date=datetime(2026, 1, day, tzinfo=timezone.utc),
+        attribution_trailers=attribution_trailers or [],
+    )
+
+
+def test_week_below_min_commits_has_empty_origin_distribution():
+    # Week A (Jan 1): a single AI-tagged commit — below MIN_COMMITS_FOR_RATIO.
+    # Week B (Jan 15-17): three human commits — a two-week gap safely clears
+    # any ISO-week-boundary ambiguity between the two groups.
+    commits = [
+        _commit(1, attribution_trailers=["copilot@users.noreply.github.com"]),
+        _commit(15),
+        _commit(16),
+        _commit(17),
+    ]
+    result = calculate_activity_timeline(commits, churn_days=14)
+    assert result is not None
+
+    week_a = next(w for w in result.weeks if w.commits == 1)
+    assert week_a.origin_distribution == {}
+    assert week_a.stabilization_ratio is None
+
+
+def test_week_at_min_commits_has_populated_origin_distribution():
+    commits = [
+        _commit(1, attribution_trailers=["copilot@users.noreply.github.com"]),
+        _commit(2),
+        _commit(3),
+        _commit(15),
+    ]
+    assert len([c for c in commits if c.date.day <= 3]) == MIN_COMMITS_FOR_RATIO
+
+    result = calculate_activity_timeline(commits, churn_days=14)
+    assert result is not None
+
+    week_a = next(w for w in result.weeks if w.commits == MIN_COMMITS_FOR_RATIO)
+    assert sum(week_a.origin_distribution.values()) == MIN_COMMITS_FOR_RATIO
+    assert week_a.origin_distribution.get("AI_ASSISTED") == 1
+
+
+if __name__ == "__main__":
+    tests = [fn for name, fn in globals().items() if name.startswith("test_")]
+    failed = 0
+    for fn in tests:
+        try:
+            fn()
+            print(f"ok  {fn.__name__}")
+        except AssertionError:
+            failed += 1
+            print(f"FAIL {fn.__name__}")
+    if failed:
+        print(f"\n{failed} failure(s)")
+        sys.exit(1)
+    print(f"\n{len(tests)} tests passed")