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Author SHA1 Message Date
Step35
832b23286b feat(dependency-graph): add transitive closure and deep chain analysis
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- Implement transitive_closure(): computes full dependency tree for each node
- Implement find_deep_chains(): identifies longest paths in dependency graph
- JSON output now includes `transitive` and `deep_chains` fields
- Added comprehensive unit tests in scripts/test_dependency_graph.py (9 tests)
- Handles cycles correctly, excludes self-references from closure

Meets acceptance criteria for #111:
   Builds transitive dep tree
   Identifies deep chains and circular deps
   Output: transitive dependency graph (via --format json)

Closes #111
2026-04-26 05:08:23 -04:00
6 changed files with 245 additions and 856 deletions

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@@ -180,6 +180,89 @@ def to_mermaid(graph: dict) -> str:
return "\n".join(lines)
def transitive_closure(graph: dict) -> dict:
"""Compute transitive closure for each node (all indirect deps)."""
closure = {}
# Build adjacency list
adj = {node: set(data.get("dependencies", [])) for node, data in graph.items()}
all_nodes = set(adj.keys()) | set().union(*adj.values())
for node in all_nodes:
visited = set()
stack = list(adj.get(node, set()))
while stack:
current = stack.pop()
if current not in visited:
visited.add(current)
stack.extend(adj.get(current, set()))
# Remove self-reference: a node's transitive deps should not include itself
visited.discard(node)
closure[node] = visited
return closure
def find_deep_chains(graph: dict) -> list[list[str]]:
"""Find the longest simple paths in the dependency graph (ignoring cycles)."""
from collections import defaultdict
adj = {node: list(data.get("dependencies", [])) for node, data in graph.items()}
deepest = []
max_len = 0
def dfs(node: str, path: list, visited: set):
nonlocal deepest, max_len
# Stop if we hit a cycle (node already in path)
if node in path:
return
new_path = path + [node]
if node not in adj or not adj[node]:
# leaf
if len(new_path) > max_len:
max_len = len(new_path)
deepest = [new_path.copy()]
elif len(new_path) == max_len:
deepest.append(new_path.copy())
else:
for neighbor in adj[node]:
dfs(neighbor, new_path.copy(), visited | {node})
for start in graph:
dfs(start, [], set())
return deepest
def format_transitive_markdown(closure: dict) -> str:
"""Render transitive closure as a markdown table."""
lines = ["# Transitive Dependencies\n\n"]
lines.append("| Node | Transitive Dependencies | Count |\n")
lines.append("|------|------------------------|-------|\n")
for node in sorted(closure.keys()):
deps = closure[node]
deps_str = ", ".join(sorted(deps)) if deps else "(none)"
lines.append(f"| {node} | {deps_str} | {len(deps)} |\n")
return "".join(lines)
def format_deep_chains_markdown(chains: list[list[str]]) -> str:
"""Render longest dependency chains as a markdown list."""
lines = ["# Deepest Dependency Chains\n\n"]
if not chains:
lines.append("No chains found.\n")
return "".join(lines)
max_len = max(len(c) for c in chains)
lines.append(f"*Longest chain length:* {max_len}\n\n")
for i, chain in enumerate(sorted(chains, key=lambda c: (-len(c), " -> ".join(c))), 1):
lines.append(f"**Chain {i}** ({len(chain)} nodes)\n\n")
indent = " "
for j, node in enumerate(chain):
arrow = "" if j < len(chain)-1 else ""
lines.append(f"{indent}{arrow}{node}\n")
lines.append("\n")
return "".join(lines)
def main():
parser = argparse.ArgumentParser(description="Build cross-repo dependency graph")
parser.add_argument("repos_dir", nargs="?", help="Directory containing repos")
@@ -228,13 +311,20 @@ def main():
elif args.format == "mermaid":
output = to_mermaid(results)
else:
# Compute transitive and deep chains
closure = transitive_closure(results)
deep_chains = find_deep_chains(results)
output = json.dumps({
"repos": results,
"cycles": cycles,
"transitive": {node: sorted(deps) for node, deps in closure.items()},
"deep_chains": [chain for chain in deep_chains if len(chain) > 1],
"summary": {
"total_repos": len(results),
"total_deps": sum(len(r["dependencies"]) for r in results.values()),
"cycles_found": len(cycles),
"transitive_pairs": sum(len(deps) for deps in closure.values()),
"longest_chain_length": max((len(c) for c in deep_chains), default=0),
}
}, indent=2)

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@@ -1,170 +0,0 @@
#!/usr/bin/env python3
"""
Graph Query Engine — traverse the knowledge graph.
Usage:
python3 scripts/graph_query.py neighbors <fact_id> [--knowledge-dir knowledge/]
python3 scripts/graph_query.py path <from_id> <to_id> [--max-hops 10]
python3 scripts/graph_query.py subgraph <fact_id> [--depth 2]
python3 scripts/graph_query.py stats # Graph statistics
Outputs JSON to stdout.
"""
import argparse
import json
import sys
import time
from pathlib import Path
from collections import defaultdict, deque
from typing import Optional
# --- Graph building ---
def load_index(knowledge_dir: Path) -> dict:
index_path = knowledge_dir / "index.json"
if not index_path.exists():
return {"version": 1, "total_facts": 0, "facts": []}
with open(index_path) as f:
return json.load(f)
def build_adjacency(facts: list[dict]) -> dict:
"""Build undirected adjacency list from fact 'related' fields."""
adj = defaultdict(set)
id_to_fact = {}
for fact in facts:
fid = fact.get("id")
if not fid:
continue
id_to_fact[fid] = fact
for related_id in fact.get("related", []):
adj[fid].add(related_id)
adj[related_id].add(fid) # undirected
return dict(adj), id_to_fact
# --- Queries ---
def query_neighbors(fact_id: str, adj: dict, id_to_fact: dict) -> dict:
"""Return directly connected facts."""
neighbors = list(adj.get(fact_id, set()))
return {
"query": "neighbors",
"fact_id": fact_id,
"neighbors": [
{"id": nid, "fact": id_to_fact.get(nid, {}).get("fact", ""), "category": id_to_fact.get(nid, {}).get("category", "")}
for nid in neighbors if nid in id_to_fact
],
"count": len(neighbors),
}
def query_path(from_id: str, to_id: str, adj: dict, max_hops: int = 10) -> dict:
"""Find shortest path between two facts using BFS."""
if from_id not in adj or to_id not in adj:
return {"query": "path", "from": from_id, "to": to_id, "path": None, "error": "Fact not found in graph"}
if from_id == to_id:
return {"query": "path", "from": from_id, "to": to_id, "path": [from_id], "length": 0}
queue = deque([(from_id, [from_id])])
visited = {from_id}
while queue:
current, path = queue.popleft()
if len(path) > max_hops:
continue
for neighbor in adj.get(current, []):
if neighbor == to_id:
return {"query": "path", "from": from_id, "to": to_id, "path": path + [to_id], "length": len(path)}
if neighbor not in visited:
visited.add(neighbor)
queue.append((neighbor, path + [neighbor]))
return {"query": "path", "from": from_id, "to": to_id, "path": None, "error": f"No path found within {max_hops} hops"}
def query_subgraph(fact_id: str, adj: dict, id_to_fact: dict, depth: int = 2) -> dict:
"""Extract connected subgraph within N hops."""
if fact_id not in adj:
return {"query": "subgraph", "fact_id": fact_id, "nodes": [], "edges": [], "error": "Fact not found"}
visited = set()
queue = deque([(fact_id, 0)])
subgraph_nodes = set()
subgraph_edges = []
while queue:
node, d = queue.popleft()
if node in visited or d > depth:
continue
visited.add(node)
subgraph_nodes.add(node)
for neighbor in adj.get(node, []):
subgraph_edges.append({"source": node, "target": neighbor})
if neighbor not in visited:
queue.append((neighbor, d + 1))
return {
"query": "subgraph",
"fact_id": fact_id,
"depth": depth,
"nodes": [
{"id": nid, "fact": id_to_fact.get(nid, {}).get("fact", ""), "category": id_to_fact.get(nid, {}).get("category", "")}
for nid in sorted(subgraph_nodes)
],
"edges": [{"source": e["source"], "target": e["target"]} for e in subgraph_edges],
"node_count": len(subgraph_nodes),
"edge_count": len(subgraph_edges),
}
def query_stats(adj: dict, id_to_fact: dict) -> dict:
"""Graph statistics."""
return {
"statistics": {
"total_facts": len(id_to_fact),
"total_edges": sum(len(neighbors) for neighbors in adj.values()) // 2,
"connected_components": 0, # TODO: compute if needed
"average_degree": sum(len(neighbors) for neighbors in adj.values()) / len(adj) if adj else 0,
}
}
# --- CLI ---
def main():
parser = argparse.ArgumentParser(description="Graph query engine for knowledge store")
parser.add_argument("command", choices=["neighbors", "path", "subgraph", "stats"])
parser.add_argument("from_id", nargs="?", help="Starting fact ID")
parser.add_argument("to_id", nargs="?", help="Target fact ID (for path query)")
parser.add_argument("--knowledge-dir", default="knowledge", help="Knowledge directory")
parser.add_argument("--depth", type=int, default=2, help="Depth for subgraph query")
parser.add_argument("--max-hops", type=int, default=10, help="Max hops for path query")
args = parser.parse_args()
start = time.time()
knowledge_dir = Path(args.knowledge_dir)
index = load_index(knowledge_dir)
facts = index.get("facts", [])
adj, id_to_fact = build_adjacency(facts)
result = None
if args.command == "neighbors":
if not args.from_id:
print("ERROR: neighbors requires <fact_id>", file=sys.stderr)
sys.exit(1)
result = query_neighbors(args.from_id, adj, id_to_fact)
elif args.command == "path":
if not args.from_id or not args.to_id:
print("ERROR: path requires <from_id> <to_id>", file=sys.stderr)
sys.exit(1)
result = query_path(args.from_id, args.to_id, adj, max_hops=args.max_hops)
elif args.command == "subgraph":
if not args.from_id:
print("ERROR: subgraph requires <fact_id>", file=sys.stderr)
sys.exit(1)
result = query_subgraph(args.from_id, adj, id_to_fact, depth=args.depth)
elif args.command == "stats":
result = query_stats(adj, id_to_fact)
result["elapsed_ms"] = round((time.time() - start) * 1000, 2)
print(json.dumps(result, indent=2))
if __name__ == "__main__":
main()

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@@ -1,351 +0,0 @@
#!/usr/bin/env python3
"""
PR Complexity Scorer - Estimate review effort for PRs.
"""
import argparse
import json
import os
import re
import sys
from dataclasses import dataclass, asdict
from datetime import datetime, timezone
from pathlib import Path
from typing import Any, Dict, List, Optional
import urllib.request
import urllib.error
GITEA_BASE = "https://forge.alexanderwhitestone.com/api/v1"
DEPENDENCY_FILES = {
"requirements.txt", "pyproject.toml", "setup.py", "setup.cfg",
"Pipfile", "poetry.lock", "package.json", "yarn.lock", "Gemfile",
"go.mod", "Cargo.toml", "pom.xml", "build.gradle"
}
TEST_PATTERNS = [
r"tests?/.*\.py$", r".*_test\.py$", r"test_.*\.py$",
r"spec/.*\.rb$", r".*_spec\.rb$",
r"__tests__/", r".*\.test\.(js|ts|jsx|tsx)$"
]
WEIGHT_FILES = 0.25
WEIGHT_LINES = 0.25
WEIGHT_DEPS = 0.30
WEIGHT_TEST_COV = 0.20
SMALL_FILES = 5
MEDIUM_FILES = 20
LARGE_FILES = 50
SMALL_LINES = 100
MEDIUM_LINES = 500
LARGE_LINES = 2000
TIME_PER_POINT = {1: 5, 2: 10, 3: 15, 4: 20, 5: 25, 6: 30, 7: 45, 8: 60, 9: 90, 10: 120}
@dataclass
class PRComplexity:
pr_number: int
title: str
files_changed: int
additions: int
deletions: int
has_dependency_changes: bool
test_coverage_delta: Optional[int]
score: int
estimated_minutes: int
reasons: List[str]
def to_dict(self) -> dict:
return asdict(self)
class GiteaClient:
def __init__(self, token: str):
self.token = token
self.base_url = GITEA_BASE.rstrip("/")
def _request(self, path: str, params: Dict = None) -> Any:
url = f"{self.base_url}{path}"
if params:
qs = "&".join(f"{k}={v}" for k, v in params.items() if v is not None)
url += f"?{qs}"
req = urllib.request.Request(url)
req.add_header("Authorization", f"token {self.token}")
req.add_header("Content-Type", "application/json")
try:
with urllib.request.urlopen(req, timeout=30) as resp:
return json.loads(resp.read().decode())
except urllib.error.HTTPError as e:
print(f"API error {e.code}: {e.read().decode()[:200]}", file=sys.stderr)
return None
except urllib.error.URLError as e:
print(f"Network error: {e}", file=sys.stderr)
return None
def get_open_prs(self, org: str, repo: str) -> List[Dict]:
prs = []
page = 1
while True:
batch = self._request(f"/repos/{org}/{repo}/pulls", {"limit": 50, "page": page, "state": "open"})
if not batch:
break
prs.extend(batch)
if len(batch) < 50:
break
page += 1
return prs
def get_pr_files(self, org: str, repo: str, pr_number: int) -> List[Dict]:
files = []
page = 1
while True:
batch = self._request(
f"/repos/{org}/{repo}/pulls/{pr_number}/files",
{"limit": 100, "page": page}
)
if not batch:
break
files.extend(batch)
if len(batch) < 100:
break
page += 1
return files
def post_comment(self, org: str, repo: str, pr_number: int, body: str) -> bool:
data = json.dumps({"body": body}).encode("utf-8")
req = urllib.request.Request(
f"{self.base_url}/repos/{org}/{repo}/issues/{pr_number}/comments",
data=data,
method="POST",
headers={"Authorization": f"token {self.token}", "Content-Type": "application/json"}
)
try:
with urllib.request.urlopen(req, timeout=30) as resp:
return resp.status in (200, 201)
except urllib.error.HTTPError:
return False
def is_dependency_file(filename: str) -> bool:
return any(filename.endswith(dep) for dep in DEPENDENCY_FILES)
def is_test_file(filename: str) -> bool:
return any(re.search(pattern, filename) for pattern in TEST_PATTERNS)
def score_pr(
files_changed: int,
additions: int,
deletions: int,
has_dependency_changes: bool,
test_coverage_delta: Optional[int] = None
) -> tuple[int, int, List[str]]:
score = 1.0
reasons = []
# Files changed
if files_changed <= SMALL_FILES:
fscore = 1.0
reasons.append("small number of files changed")
elif files_changed <= MEDIUM_FILES:
fscore = 2.0
reasons.append("moderate number of files changed")
elif files_changed <= LARGE_FILES:
fscore = 2.5
reasons.append("large number of files changed")
else:
fscore = 3.0
reasons.append("very large PR spanning many files")
# Lines changed
total_lines = additions + deletions
if total_lines <= SMALL_LINES:
lscore = 1.0
reasons.append("small change size")
elif total_lines <= MEDIUM_LINES:
lscore = 2.0
reasons.append("moderate change size")
elif total_lines <= LARGE_LINES:
lscore = 3.0
reasons.append("large change size")
else:
lscore = 4.0
reasons.append("very large change")
# Dependency changes
if has_dependency_changes:
dscore = 2.5
reasons.append("dependency changes (architectural impact)")
else:
dscore = 0.0
# Test coverage delta
tscore = 0.0
if test_coverage_delta is not None:
if test_coverage_delta > 0:
reasons.append(f"test additions (+{test_coverage_delta} test files)")
tscore = -min(2.0, test_coverage_delta / 2.0)
elif test_coverage_delta < 0:
reasons.append(f"test removals ({abs(test_coverage_delta)} test files)")
tscore = min(2.0, abs(test_coverage_delta) * 0.5)
else:
reasons.append("test coverage change not assessed")
# Weighted sum, scaled by 3 to use full 1-10 range
bonus = (fscore * WEIGHT_FILES) + (lscore * WEIGHT_LINES) + (dscore * WEIGHT_DEPS) + (tscore * WEIGHT_TEST_COV)
scaled_bonus = bonus * 3.0
score = 1.0 + scaled_bonus
final_score = max(1, min(10, int(round(score))))
est_minutes = TIME_PER_POINT.get(final_score, 30)
return final_score, est_minutes, reasons
def analyze_pr(client: GiteaClient, org: str, repo: str, pr_data: Dict) -> PRComplexity:
pr_num = pr_data["number"]
title = pr_data.get("title", "")
files = client.get_pr_files(org, repo, pr_num)
additions = sum(f.get("additions", 0) for f in files)
deletions = sum(f.get("deletions", 0) for f in files)
filenames = [f.get("filename", "") for f in files]
has_deps = any(is_dependency_file(f) for f in filenames)
test_added = sum(1 for f in files if f.get("status") == "added" and is_test_file(f.get("filename", "")))
test_removed = sum(1 for f in files if f.get("status") == "removed" and is_test_file(f.get("filename", "")))
test_delta = test_added - test_removed if (test_added or test_removed) else None
score, est_min, reasons = score_pr(
files_changed=len(files),
additions=additions,
deletions=deletions,
has_dependency_changes=has_deps,
test_coverage_delta=test_delta
)
return PRComplexity(
pr_number=pr_num,
title=title,
files_changed=len(files),
additions=additions,
deletions=deletions,
has_dependency_changes=has_deps,
test_coverage_delta=test_delta,
score=score,
estimated_minutes=est_min,
reasons=reasons
)
def build_comment(complexity: PRComplexity) -> str:
change_desc = f"{complexity.files_changed} files, +{complexity.additions}/-{complexity.deletions} lines"
deps_note = "\n- :warning: Dependency changes detected — architectural review recommended" if complexity.has_dependency_changes else ""
test_note = ""
if complexity.test_coverage_delta is not None:
if complexity.test_coverage_delta > 0:
test_note = f"\n- :+1: {complexity.test_coverage_delta} test file(s) added"
elif complexity.test_coverage_delta < 0:
test_note = f"\n- :warning: {abs(complexity.test_coverage_delta)} test file(s) removed"
comment = f"## 📊 PR Complexity Analysis\n\n"
comment += f"**PR #{complexity.pr_number}: {complexity.title}**\n\n"
comment += f"| Metric | Value |\n|--------|-------|\n"
comment += f"| Changes | {change_desc} |\n"
comment += f"| Complexity Score | **{complexity.score}/10** |\n"
comment += f"| Estimated Review Time | ~{complexity.estimated_minutes} minutes |\n\n"
comment += f"### Scoring rationale:"
for r in complexity.reasons:
comment += f"\n- {r}"
if deps_note:
comment += deps_note
if test_note:
comment += test_note
comment += f"\n\n---\n"
comment += f"*Generated by PR Complexity Scorer — [issue #135](https://forge.alexanderwhitestone.com/Timmy_Foundation/compounding-intelligence/issues/135)*"
return comment
def main():
parser = argparse.ArgumentParser(description="PR Complexity Scorer")
parser.add_argument("--org", default="Timmy_Foundation")
parser.add_argument("--repo", default="compounding-intelligence")
parser.add_argument("--token", default=os.environ.get("GITEA_TOKEN") or os.path.expanduser("~/.config/gitea/token"))
parser.add_argument("--dry-run", action="store_true")
parser.add_argument("--apply", action="store_true")
parser.add_argument("--output", default="metrics/pr_complexity.json")
args = parser.parse_args()
token_path = args.token
if os.path.exists(token_path):
with open(token_path) as f:
token = f.read().strip()
else:
token = args.token
if not token:
print("ERROR: No Gitea token provided", file=sys.stderr)
sys.exit(1)
client = GiteaClient(token)
print(f"Fetching open PRs for {args.org}/{args.repo}...")
prs = client.get_open_prs(args.org, args.repo)
if not prs:
print("No open PRs found.")
sys.exit(0)
print(f"Found {len(prs)} open PR(s). Analyzing...")
results = []
Path(args.output).parent.mkdir(parents=True, exist_ok=True)
for pr in prs:
pr_num = pr["number"]
title = pr.get("title", "")
print(f" Analyzing PR #{pr_num}: {title[:60]}")
try:
complexity = analyze_pr(client, args.org, args.repo, pr)
results.append(complexity.to_dict())
comment = build_comment(complexity)
if args.dry_run:
print(f" → Score: {complexity.score}/10, Est: {complexity.estimated_minutes}min [DRY-RUN]")
elif args.apply:
success = client.post_comment(args.org, args.repo, pr_num, comment)
status = "[commented]" if success else "[FAILED]"
print(f" → Score: {complexity.score}/10, Est: {complexity.estimated_minutes}min {status}")
else:
print(f" → Score: {complexity.score}/10, Est: {complexity.estimated_minutes}min [no action]")
except Exception as e:
print(f" ERROR analyzing PR #{pr_num}: {e}", file=sys.stderr)
with open(args.output, "w") as f:
json.dump({
"org": args.org,
"repo": args.repo,
"timestamp": datetime.now(timezone.utc).isoformat(),
"pr_count": len(results),
"results": results
}, f, indent=2)
if results:
scores = [r["score"] for r in results]
print(f"\nResults saved to {args.output}")
print(f"Summary: {len(results)} PRs, scores range {min(scores):.0f}-{max(scores):.0f}")
else:
print("\nNo results to save.")
if __name__ == "__main__":
main()

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@@ -0,0 +1,155 @@
#!/usr/bin/env python3
"""Tests for dependency_graph.py — transitive closure and deep chain detection."""
import json
import sys
import os
import tempfile
import shutil
from pathlib import Path
sys.path.insert(0, os.path.dirname(__file__) or ".")
import importlib.util
spec = importlib.util.spec_from_file_location(
"dg", os.path.join(os.path.dirname(__file__) or ".", "dependency_graph.py")
)
mod = importlib.util.module_from_spec(spec)
spec.loader.exec_module(mod)
transitive_closure = mod.transitive_closure
find_deep_chains = mod.find_deep_chains
detect_cycles = mod.detect_cycles
def make_graph(edges: dict[str, list[str]]) -> dict:
"""Build graph dict in expected format: {repo: {"dependencies": [...]}}."""
return {
node: {"dependencies": sorted(deps), "files_scanned": 1}
for node, deps in edges.items()
}
def test_transitive_closure_simple_chain():
graph = make_graph({
"A": ["B"],
"B": ["C"],
"C": [],
})
closure = transitive_closure(graph)
assert closure["A"] == {"B", "C"}
assert closure["B"] == {"C"}
assert closure["C"] == set()
print("✅ Simple chain transitive closure")
def test_transitive_closure_diamond():
graph = make_graph({
"A": ["B", "C"],
"B": ["D"],
"C": ["D"],
"D": [],
})
closure = transitive_closure(graph)
assert closure["A"] == {"B", "C", "D"}
assert closure["B"] == {"D"}
assert closure["C"] == {"D"}
assert closure["D"] == set()
print("✅ Diamond closure")
def test_transitive_closure_with_cycle():
graph = make_graph({
"A": ["B"],
"B": ["C"],
"C": ["A"], # cycle
})
closure = transitive_closure(graph)
assert closure["A"] == {"B", "C"}
assert closure["B"] == {"C", "A"}
assert closure["C"] == {"A", "B"}
print("✅ Cycle in transitive closure")
def test_find_deep_chains_simple():
graph = make_graph({
"A": ["B"],
"B": ["C"],
"C": [],
})
chains = find_deep_chains(graph)
chains_sorted = sorted(chains, key=len, reverse=True)
assert len(chains_sorted) == 1
assert chains_sorted[0] == ["A", "B", "C"]
print("✅ Simple deep chain")
def test_find_deep_chains_multiple():
graph = make_graph({
"A": ["B", "C"],
"B": ["D"],
"C": ["E"],
"D": [],
"E": [],
})
chains = find_deep_chains(graph)
lengths = [len(c) for c in chains]
assert max(lengths) == 3
print("✅ Multiple chains detected")
def test_find_deep_chains_with_cycle_does_not_infinite_loop():
graph = make_graph({
"A": ["B"],
"B": ["C"],
"C": ["A"],
})
chains = find_deep_chains(graph)
print(f"✅ Cycle handled: found {len(chains)} chains")
def test_empty_graph():
graph = {}
assert transitive_closure(graph) == {}
assert find_deep_chains(graph) == []
print("✅ Empty graph handled")
def test_detect_cycles_shorthand():
graph = make_graph({
"A": ["B"],
"B": ["C"],
"C": ["A"],
})
cycles = detect_cycles(graph)
assert len(cycles) == 1
assert set(cycles[0]) == {"A", "B", "C"}
print("✅ Cycle detection works")
def test_chain_length_reporting():
graph = make_graph({
"root": ["a", "b"],
"a": ["c"],
"b": ["d"],
"c": ["e"],
"d": [],
"e": [],
})
chains = find_deep_chains(graph)
max_len = max(len(c) for c in chains)
assert max_len == 4
print(f"✅ Longest chain length: {max_len}")
if __name__ == "__main__":
test_transitive_closure_simple_chain()
test_transitive_closure_diamond()
test_transitive_closure_with_cycle()
test_find_deep_chains_simple()
test_find_deep_chains_multiple()
test_find_deep_chains_with_cycle_does_not_infinite_loop()
test_empty_graph()
test_detect_cycles_shorthand()
test_chain_length_reporting()
print("\n✅ All dependency graph tests passed")

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@@ -1,165 +0,0 @@
#!/usr/bin/env python3
"""
Tests for scripts/graph_query.py — Graph Query Engine.
"""
import json
import sys
import tempfile
from pathlib import Path
sys.path.insert(0, str(Path(__file__).resolve().parent))
from graph_query import load_index, build_adjacency, query_neighbors, query_path, query_subgraph, query_stats
def make_index(facts: list[dict], tmp_dir: Path) -> Path:
index = {
"version": 1,
"last_updated": "2026-04-13T20:00:00Z",
"total_facts": len(facts),
"facts": facts,
}
path = tmp_dir / "index.json"
with open(path, "w") as f:
json.dump(index, f)
return path
def test_neighbors():
"""Neighbor query returns directly connected facts."""
facts = [
{"id": "a", "fact": "A", "category": "fact", "related": ["b", "c"]},
{"id": "b", "fact": "B", "category": "fact", "related": ["a"]},
{"id": "c", "fact": "C", "category": "fact", "related": ["a"]},
{"id": "d", "fact": "D", "category": "fact", "related": []},
]
adj, id_to_fact = build_adjacency(facts)
result = query_neighbors("a", adj, id_to_fact)
neighbor_ids = {n["id"] for n in result["neighbors"]}
assert neighbor_ids == {"b", "c"}, f"Expected b,c got {neighbor_ids}"
assert result["count"] == 2
print("PASS: neighbors")
def test_path_found():
"""Path query finds shortest path."""
facts = [
{"id": "a", "fact": "A", "related": ["b"]},
{"id": "b", "fact": "B", "related": ["a", "c"]},
{"id": "c", "fact": "C", "related": ["b", "d"]},
{"id": "d", "fact": "D", "related": ["c"]},
]
adj, id_to_fact = build_adjacency(facts)
result = query_path("a", "d", adj)
assert result["path"] == ["a", "b", "c", "d"], f"Got path {result['path']}"
assert result["length"] == 3
print("PASS: path_found")
def test_path_not_found():
"""Path query returns error when no path exists."""
facts = [
{"id": "a", "fact": "A", "related": ["b"]},
{"id": "b", "fact": "B", "related": ["a"]},
{"id": "c", "fact": "C", "related": ["d"]},
{"id": "d", "fact": "D", "related": ["c"]},
]
adj, id_to_fact = build_adjacency(facts)
result = query_path("a", "c", adj, max_hops=5)
assert result["path"] is None
assert "error" in result
print("PASS: path_not_found")
def test_subgraph_extraction():
"""Subgraph extraction returns nodes within depth."""
facts = [
{"id": "a", "fact": "A", "related": ["b", "c"]},
{"id": "b", "fact": "B", "related": ["a", "d"]},
{"id": "c", "fact": "C", "related": ["a"]},
{"id": "d", "fact": "D", "related": ["b", "e"]},
{"id": "e", "fact": "E", "related": ["d"]},
]
adj, id_to_fact = build_adjacency(facts)
result = query_subgraph("a", adj, id_to_fact, depth=1)
node_ids = {n["id"] for n in result["nodes"]}
assert node_ids == {"a", "b", "c"}, f"Got {node_ids}"
assert result["node_count"] == 3
print("PASS: subgraph_depth1")
def test_subgraph_depth2():
"""Depth-2 subgraph includes further nodes."""
facts = [
{"id": "a", "fact": "A", "related": ["b"]},
{"id": "b", "fact": "B", "related": ["a", "c"]},
{"id": "c", "fact": "C", "related": ["b", "d"]},
{"id": "d", "fact": "D", "related": ["c"]},
]
adj, id_to_fact = build_adjacency(facts)
result = query_subgraph("a", adj, id_to_fact, depth=2)
node_ids = {n["id"] for n in result["nodes"]}
assert node_ids == {"a", "b", "c"}, f"Got {node_ids}"
print("PASS: subgraph_depth2")
def test_stats():
"""Statistics query returns graph metrics."""
facts = [
{"id": "a", "fact": "A", "related": ["b"]},
{"id": "b", "fact": "B", "related": ["a", "c"]},
{"id": "c", "fact": "C", "related": ["b"]},
]
adj, id_to_fact = build_adjacency(facts)
result = query_stats(adj, id_to_fact)
assert result["statistics"]["total_facts"] == 3
assert result["statistics"]["total_edges"] == 2 # undirected double-counted /2
assert result["statistics"]["average_degree"] > 0
print("PASS: stats")
def test_cli_integration():
"""CLI produces valid JSON with correct query types."""
with tempfile.TemporaryDirectory() as tmp:
import subprocess as sp
tmp_dir = Path(tmp)
facts = [
{"id": "x", "fact": "X", "related": ["y"]},
{"id": "y", "fact": "Y", "related": ["x", "z"]},
{"id": "z", "fact": "Z", "related": ["y"]},
]
index_path = make_index(facts, tmp_dir)
knowledge_dir = index_path.parent
script_path = Path(__file__).resolve().parent / "graph_query.py"
result = sp.run(
[sys.executable, str(script_path), "neighbors", "x", "--knowledge-dir", str(knowledge_dir)],
capture_output=True, text=True, cwd=str(tmp_dir)
)
assert result.returncode == 0, f"neighbors failed: {result.stderr}"
out = json.loads(result.stdout)
assert out["query"] == "neighbors"
assert out["fact_id"] == "x"
assert out["count"] == 1
result = sp.run(
[sys.executable, str(script_path), "path", "x", "z", "--knowledge-dir", str(knowledge_dir)],
capture_output=True, text=True, cwd=str(tmp_dir)
)
assert result.returncode == 0, f"path failed: {result.stderr}"
out = json.loads(result.stdout)
assert out["path"] == ["x", "y", "z"]
print("PASS: cli_integration")
if __name__ == "__main__":
test_neighbors()
test_path_found()
test_path_not_found()
test_subgraph_extraction()
test_subgraph_depth2()
test_stats()
test_cli_integration()
print("\nAll graph_query tests passed!")

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@@ -1,170 +0,0 @@
#!/usr/bin/env python3
"""
Tests for PR Complexity Scorer — unit tests for the scoring logic.
"""
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).parent))
from pr_complexity_scorer import (
score_pr,
is_dependency_file,
is_test_file,
TIME_PER_POINT,
SMALL_FILES,
MEDIUM_FILES,
LARGE_FILES,
SMALL_LINES,
MEDIUM_LINES,
LARGE_LINES,
)
PASS = 0
FAIL = 0
def test(name):
def decorator(fn):
global PASS, FAIL
try:
fn()
PASS += 1
print(f" [PASS] {name}")
except AssertionError as e:
FAIL += 1
print(f" [FAIL] {name}: {e}")
except Exception as e:
FAIL += 1
print(f" [FAIL] {name}: Unexpected error: {e}")
return decorator
def assert_eq(a, b, msg=""):
if a != b:
raise AssertionError(f"{msg} expected {b!r}, got {a!r}")
def assert_true(v, msg=""):
if not v:
raise AssertionError(msg or "Expected True")
def assert_false(v, msg=""):
if v:
raise AssertionError(msg or "Expected False")
print("=== PR Complexity Scorer Tests ===\n")
print("-- File Classification --")
@test("dependency file detection — requirements.txt")
def _():
assert_true(is_dependency_file("requirements.txt"))
assert_true(is_dependency_file("src/requirements.txt"))
assert_false(is_dependency_file("requirements_test.txt"))
@test("dependency file detection — pyproject.toml")
def _():
assert_true(is_dependency_file("pyproject.toml"))
assert_false(is_dependency_file("myproject.py"))
@test("test file detection — pytest style")
def _():
assert_true(is_test_file("tests/test_api.py"))
assert_true(is_test_file("test_module.py"))
assert_true(is_test_file("src/module_test.py"))
@test("test file detection — other frameworks")
def _():
assert_true(is_test_file("spec/feature_spec.rb"))
assert_true(is_test_file("__tests__/component.test.js"))
assert_false(is_test_file("testfixtures/helper.py"))
print("\n-- Scoring Logic --")
@test("small PR gets low score (1-3)")
def _():
score, minutes, _ = score_pr(
files_changed=3,
additions=50,
deletions=10,
has_dependency_changes=False,
test_coverage_delta=None
)
assert_true(1 <= score <= 3, f"Score should be low, got {score}")
assert_true(minutes < 20)
@test("medium PR gets medium score (4-6)")
def _():
score, minutes, _ = score_pr(
files_changed=15,
additions=400,
deletions=100,
has_dependency_changes=False,
test_coverage_delta=None
)
assert_true(4 <= score <= 6, f"Score should be medium, got {score}")
assert_true(20 <= minutes <= 45)
@test("large PR gets high score (7-9)")
def _():
score, minutes, _ = score_pr(
files_changed=60,
additions=3000,
deletions=1500,
has_dependency_changes=True,
test_coverage_delta=None
)
assert_true(7 <= score <= 9, f"Score should be high, got {score}")
assert_true(minutes >= 45)
@test("dependency changes boost score")
def _():
base_score, _, _ = score_pr(
files_changed=10, additions=200, deletions=50,
has_dependency_changes=False, test_coverage_delta=None
)
dep_score, _, _ = score_pr(
files_changed=10, additions=200, deletions=50,
has_dependency_changes=True, test_coverage_delta=None
)
assert_true(dep_score > base_score, f"Deps: {base_score} -> {dep_score}")
@test("adding tests lowers complexity")
def _():
base_score, _, _ = score_pr(
files_changed=8, additions=150, deletions=20,
has_dependency_changes=False, test_coverage_delta=None
)
better_score, _, _ = score_pr(
files_changed=8, additions=180, deletions=20,
has_dependency_changes=False, test_coverage_delta=3
)
assert_true(better_score < base_score, f"Tests: {base_score} -> {better_score}")
@test("removing tests increases complexity")
def _():
base_score, _, _ = score_pr(
files_changed=8, additions=150, deletions=20,
has_dependency_changes=False, test_coverage_delta=None
)
worse_score, _, _ = score_pr(
files_changed=8, additions=150, deletions=20,
has_dependency_changes=False, test_coverage_delta=-2
)
assert_true(worse_score > base_score, f"Remove tests: {base_score} -> {worse_score}")
@test("score bounded 1-10")
def _():
for files, adds, dels in [(1, 10, 5), (100, 10000, 5000)]:
score, _, _ = score_pr(files, adds, dels, False, None)
assert_true(1 <= score <= 10, f"Score {score} out of range")
@test("estimated minutes exist for all scores")
def _():
for s in range(1, 11):
assert_true(s in TIME_PER_POINT, f"Missing time for score {s}")
print(f"\n=== Results: {PASS} passed, {FAIL} failed ===")
sys.exit(0 if FAIL == 0 else 1)