"""Simplified call graph centrality for scantool."""
import re
def analyze_call_graph_simple(data: bytes, partitions: list[dict]) -> list[float]:
"""
Simplified call graph analysis - returns centrality scores for partitions.
Args:
data: Raw file bytes
partitions: List of partition dicts
Returns:
List of centrality scores (0.0-1.0) per partition
"""
try:
text = data.decode("utf-8", errors="replace")
except Exception:
return [0.0] * len(partitions)
# Extract function/class names
entities = []
# Python: def function_name(
for match in re.finditer(r"\bdef\s+(\w+)\s*\(", text):
entities.append({"name": match.group(1), "offset": match.start()})
# Python: class ClassName
for match in re.finditer(r"\bclass\s+(\w+)", text):
entities.append({"name": match.group(1), "offset": match.start()})
# JavaScript/TypeScript: function functionName(
for match in re.finditer(r"\bfunction\s+(\w+)\s*\(", text):
entities.append({"name": match.group(1), "offset": match.start()})
# C/C++: type function_name(
for match in re.finditer(r"\b(?:void|int|float|double|char|bool|auto|static)\s+(\w+)\s*\(", text):
entities.append({"name": match.group(1), "offset": match.start()})
if not entities:
return [0.0] * len(partitions)
# Build call graph (who calls whom)
entity_names = {e["name"] for e in entities}
call_counts = {name: 0 for name in entity_names}
# Count calls
for match in re.finditer(r"\b(\w+)\s*\(", text):
called = match.group(1)
if called in entity_names:
call_counts[called] += 1
# Assign centrality to partitions
centrality_scores = []
for partition in partitions:
max_centrality = 0.0
offset = partition["offset"]
# Find entities in this partition
for entity in entities:
if entity["offset"] >= offset and entity["offset"] < offset + partition["size"]:
centrality = call_counts.get(entity["name"], 0)
max_centrality = max(max_centrality, centrality)
centrality_scores.append(float(max_centrality))
return centrality_scores
