graphify/tests/test_analyze.py

"""Tests for analyze.py."""
import json
import networkx as nx
from pathlib import Path
from graphify.build import build_from_json
from graphify.cluster import cluster
from graphify.analyze import god_nodes, surprising_connections, _is_concept_node, graph_diff, _surprise_score, _file_category, _is_json_key_node

FIXTURES = Path(__file__).parent / "fixtures"


def make_graph():
    return build_from_json(json.loads((FIXTURES / "extraction.json").read_text()))


def test_god_nodes_returns_list():
    G = make_graph()
    result = god_nodes(G, top_n=3)
    assert isinstance(result, list)
    assert len(result) <= 3


def test_god_nodes_sorted_by_degree():
    G = make_graph()
    result = god_nodes(G, top_n=10)
    degrees = [r["degree"] for r in result]
    assert degrees == sorted(degrees, reverse=True)


def test_god_nodes_have_required_keys():
    G = make_graph()
    result = god_nodes(G, top_n=1)
    assert "id" in result[0]
    assert "label" in result[0]
    assert "degree" in result[0]


def test_surprising_connections_cross_source_multi_file():
    """Multi-file graph: should find cross-file edges between real entities."""
    G = make_graph()
    communities = cluster(G)
    surprises = surprising_connections(G, communities)
    assert len(surprises) > 0
    for s in surprises:
        assert s["source_files"][0] != s["source_files"][1]


def test_surprising_connections_excludes_concept_nodes():
    """Concept nodes (empty source_file) must not appear in surprises."""
    G = make_graph()
    # Add a concept node with empty source_file
    G.add_node("concept_x", label="Abstract Concept", file_type="document", source_file="")
    G.add_edge("n_transformer", "concept_x", relation="relates_to",
               confidence="INFERRED", source_file="", weight=0.5)
    communities = cluster(G)
    surprises = surprising_connections(G, communities)
    labels = [s["source"] for s in surprises] + [s["target"] for s in surprises]
    assert "Abstract Concept" not in labels


def test_surprising_connections_single_file_uses_community_bridges():
    """Single-file graph: should return cross-community edges, not empty list."""
    G = nx.Graph()
    # Build a graph with 2 clear communities + 1 bridge edge
    for i in range(5):
        G.add_node(f"a{i}", label=f"A{i}", file_type="code", source_file="single.py",
                   source_location=f"L{i}")
    for i in range(5):
        G.add_node(f"b{i}", label=f"B{i}", file_type="code", source_file="single.py",
                   source_location=f"L{i+10}")
    # Dense intra-community edges
    for i in range(4):
        G.add_edge(f"a{i}", f"a{i+1}", relation="calls", confidence="EXTRACTED",
                   source_file="single.py", weight=1.0)
    for i in range(4):
        G.add_edge(f"b{i}", f"b{i+1}", relation="calls", confidence="EXTRACTED",
                   source_file="single.py", weight=1.0)
    # One cross-community bridge
    G.add_edge("a4", "b0", relation="references", confidence="INFERRED",
               source_file="single.py", weight=0.5)

    communities = cluster(G)
    surprises = surprising_connections(G, communities)
    # Should find at least the bridge edge
    assert len(surprises) > 0


def test_surprising_connections_ambiguous_scores_higher_than_extracted():
    """AMBIGUOUS edge should score higher than an otherwise identical EXTRACTED edge."""
    G = nx.Graph()
    for nid, label, src in [
        ("a", "Alpha", "repo1/model.py"),
        ("b", "Beta", "repo2/train.py"),
        ("c", "Gamma", "repo1/data.py"),
        ("d", "Delta", "repo2/eval.py"),
    ]:
        G.add_node(nid, label=label, source_file=src, file_type="code")
    G.add_edge("a", "b", relation="calls", confidence="AMBIGUOUS", weight=1.0, source_file="repo1/model.py")
    G.add_edge("c", "d", relation="calls", confidence="EXTRACTED", weight=1.0, source_file="repo1/data.py")
    communities = {0: ["a", "c"], 1: ["b", "d"]}
    nc = {"a": 0, "c": 0, "b": 1, "d": 1}
    score_amb, _ = _surprise_score(G, "a", "b", G.edges["a", "b"], nc, "repo1/model.py", "repo2/train.py")
    score_ext, _ = _surprise_score(G, "c", "d", G.edges["c", "d"], nc, "repo1/data.py", "repo2/eval.py")
    assert score_amb > score_ext


def test_surprising_connections_cross_type_scores_higher():
    """Code↔paper edge should score higher than code↔code edge."""
    G = nx.Graph()
    for nid, label, src in [
        ("a", "Transformer", "code/model.py"),
        ("b", "FlashAttn", "papers/flash.pdf"),
        ("c", "Trainer", "code/train.py"),
        ("d", "Dataset", "code/data.py"),
    ]:
        G.add_node(nid, label=label, source_file=src, file_type="code")
    G.add_edge("a", "b", relation="references", confidence="EXTRACTED", weight=1.0, source_file="code/model.py")
    G.add_edge("c", "d", relation="calls", confidence="EXTRACTED", weight=1.0, source_file="code/train.py")
    nc = {"a": 0, "b": 1, "c": 0, "d": 0}
    score_cross, reasons_cross = _surprise_score(G, "a", "b", G.edges["a", "b"], nc, "code/model.py", "papers/flash.pdf")
    score_same, _ = _surprise_score(G, "c", "d", G.edges["c", "d"], nc, "code/train.py", "code/data.py")
    assert score_cross > score_same
    assert any("code" in r and "paper" in r for r in reasons_cross)


def _make_cross_lang_graph():
    """Helper: Python node in backend/, TypeScript node in frontend/, different communities."""
    G = nx.Graph()
    G.add_node("py_auth", label="AuthError", source_file="backend/auth.py", file_type="code")
    G.add_node("ts_member", label="Member", source_file="frontend/types.ts", file_type="code")
    G.add_node("py_a", label="ServiceA", source_file="backend/service.py", file_type="code")
    G.add_node("py_b", label="ServiceB", source_file="backend/utils.py", file_type="code")
    return G


def test_cross_language_inferred_calls_suppressed():
    """Cross-language INFERRED calls edge should score lower than same-language EXTRACTED."""
    G = _make_cross_lang_graph()
    G.add_edge("py_auth", "ts_member", relation="calls", confidence="INFERRED",
               weight=0.8, source_file="backend/auth.py")
    G.add_edge("py_a", "py_b", relation="calls", confidence="EXTRACTED",
               weight=1.0, source_file="backend/service.py")
    nc = {"py_auth": 0, "ts_member": 1, "py_a": 0, "py_b": 0}
    score_cross, _ = _surprise_score(G, "py_auth", "ts_member",
                                      G.edges["py_auth", "ts_member"], nc,
                                      "backend/auth.py", "frontend/types.ts")
    score_same, _ = _surprise_score(G, "py_a", "py_b",
                                     G.edges["py_a", "py_b"], nc,
                                     "backend/service.py", "backend/utils.py")
    assert score_cross <= score_same


def test_cross_language_inferred_uses_suppressed():
    """Cross-language INFERRED uses edge (the exact rsl-siege-manager false positive) should be suppressed."""
    G = _make_cross_lang_graph()
    G.add_edge("py_auth", "ts_member", relation="uses", confidence="INFERRED",
               weight=0.8, source_file="backend/auth.py")
    G.add_edge("py_a", "py_b", relation="calls", confidence="EXTRACTED",
               weight=1.0, source_file="backend/service.py")
    nc = {"py_auth": 0, "ts_member": 1, "py_a": 0, "py_b": 0}
    score_cross, _ = _surprise_score(G, "py_auth", "ts_member",
                                      G.edges["py_auth", "ts_member"], nc,
                                      "backend/auth.py", "frontend/types.ts")
    score_same, _ = _surprise_score(G, "py_a", "py_b",
                                     G.edges["py_a", "py_b"], nc,
                                     "backend/service.py", "backend/utils.py")
    assert score_cross <= score_same


def test_cross_language_semantically_similar_not_suppressed():
    """`semantically_similar_to` across languages is a genuine insight — must not be suppressed."""
    G = _make_cross_lang_graph()
    G.add_edge("py_auth", "ts_member", relation="semantically_similar_to",
               confidence="INFERRED", weight=0.85, source_file="backend/auth.py")
    G.add_edge("py_a", "py_b", relation="calls", confidence="EXTRACTED",
               weight=1.0, source_file="backend/service.py")
    nc = {"py_auth": 0, "ts_member": 1, "py_a": 0, "py_b": 0}
    score_sem, _ = _surprise_score(G, "py_auth", "ts_member",
                                    G.edges["py_auth", "ts_member"], nc,
                                    "backend/auth.py", "frontend/types.ts")
    score_same, _ = _surprise_score(G, "py_a", "py_b",
                                     G.edges["py_a", "py_b"], nc,
                                     "backend/service.py", "backend/utils.py")
    assert score_sem > score_same


def test_same_language_inferred_calls_not_suppressed():
    """INFERRED calls within the same language family must not be affected."""
    G = nx.Graph()
    G.add_node("py_a", label="ModuleA", source_file="src/a.py", file_type="code")
    G.add_node("py_b", label="ModuleB", source_file="src/b.py", file_type="code")
    G.add_node("py_c", label="ModuleC", source_file="src/c.py", file_type="code")
    G.add_node("py_d", label="ModuleD", source_file="src/d.py", file_type="code")
    G.add_edge("py_a", "py_b", relation="calls", confidence="INFERRED",
               weight=0.8, source_file="src/a.py")
    G.add_edge("py_c", "py_d", relation="calls", confidence="EXTRACTED",
               weight=1.0, source_file="src/c.py")
    nc = {"py_a": 0, "py_b": 1, "py_c": 0, "py_d": 1}
    score_inf, _ = _surprise_score(G, "py_a", "py_b", G.edges["py_a", "py_b"], nc,
                                    "src/a.py", "src/b.py")
    score_ext, _ = _surprise_score(G, "py_c", "py_d", G.edges["py_c", "py_d"], nc,
                                    "src/c.py", "src/d.py")
    assert score_inf > score_ext


def test_cross_language_extracted_calls_not_suppressed():
    """EXTRACTED cross-language edges are real structural facts — must not be penalised."""
    G = _make_cross_lang_graph()
    G.add_edge("py_auth", "ts_member", relation="calls", confidence="EXTRACTED",
               weight=1.0, source_file="backend/auth.py")
    nc = {"py_auth": 0, "ts_member": 1}
    score, _ = _surprise_score(G, "py_auth", "ts_member",
                                G.edges["py_auth", "ts_member"], nc,
                                "backend/auth.py", "frontend/types.ts")
    assert score >= 1


def test_surprising_connections_have_why_field():
    G = make_graph()
    communities = cluster(G)
    for s in surprising_connections(G, communities):
        assert "why" in s
        assert isinstance(s["why"], str)
        assert len(s["why"]) > 0


def test_file_category():
    assert _file_category("model.py") == "code"
    assert _file_category("flash.pdf") == "paper"
    assert _file_category("diagram.png") == "image"
    assert _file_category("notes.md") == "doc"
    # Languages added in later releases — would misclassify as "doc" without detect.py import
    assert _file_category("app.swift") == "code"
    assert _file_category("plugin.lua") == "code"
    assert _file_category("build.zig") == "code"
    assert _file_category("deploy.ps1") == "code"
    assert _file_category("server.ex") == "code"
    assert _file_category("component.jsx") == "code"
    assert _file_category("analysis.jl") == "code"
    assert _file_category("view.m") == "code"


def test_is_concept_node_empty_source():
    G = nx.Graph()
    G.add_node("c1", source_file="")
    assert _is_concept_node(G, "c1") is True


def test_is_concept_node_real_file():
    G = nx.Graph()
    G.add_node("n1", source_file="model.py")
    assert _is_concept_node(G, "n1") is False


def test_surprising_connections_have_required_keys():
    G = make_graph()
    communities = cluster(G)
    for s in surprising_connections(G, communities):
        assert "source" in s
        assert "target" in s
        assert "source_files" in s
        assert "confidence" in s


# --- graph_diff tests ---

def _make_simple_graph(nodes, edges):
    """Helper: build a small nx.Graph from node/edge specs."""
    G = nx.Graph()
    for node_id, label in nodes:
        G.add_node(node_id, label=label, source_file="test.py")
    for src, tgt, rel, conf in edges:
        G.add_edge(src, tgt, relation=rel, confidence=conf)
    return G


def test_graph_diff_new_nodes():
    G_old = _make_simple_graph([("n1", "Alpha"), ("n2", "Beta")], [])
    G_new = _make_simple_graph([("n1", "Alpha"), ("n2", "Beta"), ("n3", "Gamma")], [])
    diff = graph_diff(G_old, G_new)
    assert len(diff["new_nodes"]) == 1
    assert diff["new_nodes"][0]["id"] == "n3"
    assert diff["new_nodes"][0]["label"] == "Gamma"
    assert diff["removed_nodes"] == []
    assert "1 new node" in diff["summary"]


def test_graph_diff_removed_nodes():
    G_old = _make_simple_graph([("n1", "Alpha"), ("n2", "Beta"), ("n3", "Gamma")], [])
    G_new = _make_simple_graph([("n1", "Alpha"), ("n2", "Beta")], [])
    diff = graph_diff(G_old, G_new)
    assert diff["new_nodes"] == []
    assert len(diff["removed_nodes"]) == 1
    assert diff["removed_nodes"][0]["id"] == "n3"
    assert "removed" in diff["summary"]


def test_graph_diff_new_edges():
    nodes = [("n1", "Alpha"), ("n2", "Beta"), ("n3", "Gamma")]
    G_old = _make_simple_graph(nodes, [("n1", "n2", "calls", "EXTRACTED")])
    G_new = _make_simple_graph(
        nodes,
        [("n1", "n2", "calls", "EXTRACTED"), ("n2", "n3", "uses", "INFERRED")],
    )
    diff = graph_diff(G_old, G_new)
    assert len(diff["new_edges"]) == 1
    new_edge = diff["new_edges"][0]
    assert new_edge["relation"] == "uses"
    assert new_edge["confidence"] == "INFERRED"
    assert diff["removed_edges"] == []
    assert "new edge" in diff["summary"]


def test_graph_diff_empty_diff():
    nodes = [("n1", "Alpha"), ("n2", "Beta")]
    edges = [("n1", "n2", "calls", "EXTRACTED")]
    G_old = _make_simple_graph(nodes, edges)
    G_new = _make_simple_graph(nodes, edges)
    diff = graph_diff(G_old, G_new)
    assert diff["new_nodes"] == []
    assert diff["removed_nodes"] == []
    assert diff["new_edges"] == []
    assert diff["removed_edges"] == []
    assert diff["summary"] == "no changes"


# --- code↔doc INFERRED suppression tests ---

def _make_code_doc_graph():
    G = nx.Graph()
    G.add_node("py_fn", label="ProcessData", source_file="src/processor.py", file_type="code")
    G.add_node("md_doc", label="README Section", source_file="docs/readme.md", file_type="document")
    G.add_node("py_a", label="ServiceA", source_file="src/service.py", file_type="code")
    G.add_node("py_b", label="ServiceB", source_file="src/utils.py", file_type="code")
    return G


def test_code_doc_inferred_calls_suppressed():
    """Code→doc INFERRED calls edge should score lower than same-language EXTRACTED."""
    G = _make_code_doc_graph()
    G.add_edge("py_fn", "md_doc", relation="calls", confidence="INFERRED",
               weight=0.8, source_file="src/processor.py")
    G.add_edge("py_a", "py_b", relation="calls", confidence="EXTRACTED",
               weight=1.0, source_file="src/service.py")
    nc = {"py_fn": 0, "md_doc": 1, "py_a": 0, "py_b": 0}
    score_noise, _ = _surprise_score(G, "py_fn", "md_doc",
                                     G.edges["py_fn", "md_doc"], nc,
                                     "src/processor.py", "docs/readme.md")
    score_real, _ = _surprise_score(G, "py_a", "py_b",
                                    G.edges["py_a", "py_b"], nc,
                                    "src/service.py", "src/utils.py")
    assert score_noise <= score_real


def test_code_doc_inferred_uses_suppressed():
    """Code→doc INFERRED uses edge should score lower than same-language EXTRACTED."""
    G = _make_code_doc_graph()
    G.add_edge("py_fn", "md_doc", relation="uses", confidence="INFERRED",
               weight=0.8, source_file="src/processor.py")
    G.add_edge("py_a", "py_b", relation="calls", confidence="EXTRACTED",
               weight=1.0, source_file="src/service.py")
    nc = {"py_fn": 0, "md_doc": 1, "py_a": 0, "py_b": 0}
    score_noise, _ = _surprise_score(G, "py_fn", "md_doc",
                                     G.edges["py_fn", "md_doc"], nc,
                                     "src/processor.py", "docs/readme.md")
    score_real, _ = _surprise_score(G, "py_a", "py_b",
                                    G.edges["py_a", "py_b"], nc,
                                    "src/service.py", "src/utils.py")
    assert score_noise <= score_real


def test_code_doc_extracted_calls_not_suppressed():
    """EXTRACTED code↔doc edges are real facts — must not be penalised."""
    G = _make_code_doc_graph()
    G.add_edge("py_fn", "md_doc", relation="calls", confidence="EXTRACTED",
               weight=1.0, source_file="src/processor.py")
    nc = {"py_fn": 0, "md_doc": 1}
    score, _ = _surprise_score(G, "py_fn", "md_doc",
                               G.edges["py_fn", "md_doc"], nc,
                               "src/processor.py", "docs/readme.md")
    assert score >= 1


def test_code_doc_inferred_semantically_similar_not_suppressed():
    """`semantically_similar_to` across code↔doc is explicit LLM insight — must not be suppressed."""
    G = _make_code_doc_graph()
    G.add_edge("py_fn", "md_doc", relation="semantically_similar_to",
               confidence="INFERRED", weight=0.85, source_file="src/processor.py")
    G.add_edge("py_a", "py_b", relation="calls", confidence="EXTRACTED",
               weight=1.0, source_file="src/service.py")
    nc = {"py_fn": 0, "md_doc": 1, "py_a": 0, "py_b": 0}
    score_sem, _ = _surprise_score(G, "py_fn", "md_doc",
                                   G.edges["py_fn", "md_doc"], nc,
                                   "src/processor.py", "docs/readme.md")
    score_same, _ = _surprise_score(G, "py_a", "py_b",
                                    G.edges["py_a", "py_b"], nc,
                                    "src/service.py", "src/utils.py")
    assert score_sem > score_same


def test_code_unknown_extension_inferred_calls_suppressed():
    """_file_category falls back to 'doc' for unknown extensions, so INFERRED
    calls/uses to unknown-extension files are suppressed the same as code↔doc."""
    assert _file_category("vendor/random.xyz") == "doc"
    G = nx.Graph()
    G.add_node("py_fn", label="Handler", source_file="src/handler.py", file_type="code")
    G.add_node("unk", label="Handler", source_file="vendor/unknown.xyz", file_type="document")
    G.add_node("py_a", label="A", source_file="src/a.py", file_type="code")
    G.add_node("py_b", label="B", source_file="src/b.py", file_type="code")
    G.add_edge("py_fn", "unk", relation="calls", confidence="INFERRED",
               weight=0.8, source_file="src/handler.py")
    G.add_edge("py_a", "py_b", relation="calls", confidence="EXTRACTED",
               weight=1.0, source_file="src/a.py")
    nc = {"py_fn": 0, "unk": 1, "py_a": 0, "py_b": 0}
    score_unk, _ = _surprise_score(G, "py_fn", "unk",
                                   G.edges["py_fn", "unk"], nc,
                                   "src/handler.py", "vendor/unknown.xyz")
    score_same, _ = _surprise_score(G, "py_a", "py_b",
                                    G.edges["py_a", "py_b"], nc,
                                    "src/a.py", "src/b.py")
    assert score_unk <= score_same


def test_code_paper_inferred_calls_not_suppressed():
    """Code↔paper INFERRED calls should still surface — it is a meaningful link."""
    G = nx.Graph()
    G.add_node("py_model", label="Transformer", source_file="src/model.py", file_type="code")
    G.add_node("pdf_paper", label="Attention Is All You Need", source_file="papers/vaswani.pdf",
               file_type="paper")
    G.add_node("py_a", label="ServiceA", source_file="src/service.py", file_type="code")
    G.add_node("py_b", label="ServiceB", source_file="src/utils.py", file_type="code")
    G.add_edge("py_model", "pdf_paper", relation="calls", confidence="INFERRED",
               weight=0.8, source_file="src/model.py")
    G.add_edge("py_a", "py_b", relation="calls", confidence="EXTRACTED",
               weight=1.0, source_file="src/service.py")
    nc = {"py_model": 0, "pdf_paper": 1, "py_a": 0, "py_b": 1}
    score_cross, _ = _surprise_score(G, "py_model", "pdf_paper",
                                     G.edges["py_model", "pdf_paper"], nc,
                                     "src/model.py", "papers/vaswani.pdf")
    score_same, _ = _surprise_score(G, "py_a", "py_b",
                                    G.edges["py_a", "py_b"], nc,
                                    "src/service.py", "src/utils.py")
    assert score_cross > score_same


# --- JSON key node filtering tests ---

def test_is_json_key_node_noise_label():
    G = nx.Graph()
    G.add_node("j1", label="name", source_file="schema.json")
    assert _is_json_key_node(G, "j1") is True


def test_is_json_key_node_non_json_file():
    G = nx.Graph()
    G.add_node("n1", label="name", source_file="model.py")
    assert _is_json_key_node(G, "n1") is False


def test_is_json_key_node_real_label():
    G = nx.Graph()
    G.add_node("j2", label="UserProfile", source_file="schema.json")
    assert _is_json_key_node(G, "j2") is False


def test_god_nodes_excludes_json_noise():
    """god_nodes must not return generic JSON key nodes like 'name' or 'id'."""
    G = nx.Graph()
    # Add many edges to a real node
    G.add_node("real", label="AuthService", source_file="src/auth.py")
    # Add a noisy JSON key node with high degree
    G.add_node("json_name", label="name", source_file="schema.json")
    for i in range(8):
        n = f"peer{i}"
        G.add_node(n, label=f"Peer{i}", source_file=f"src/peer{i}.py")
        G.add_edge("json_name", n)
        G.add_edge("real", n)
    result = god_nodes(G, top_n=10)
    labels = [r["label"] for r in result]
    assert "name" not in labels
    assert "AuthService" in labels


def test_god_nodes_filter_is_case_insensitive():
    """JSON-key filter must match regardless of label casing."""
    G = nx.Graph()
    G.add_node("real", label="RealAbstraction", source_file="libs/real.py")
    for i in range(3):
        G.add_node(f"peer{i}", label=f"P{i}", source_file=f"src/p{i}.py")
        G.add_edge("real", f"peer{i}")
    for variant in ("Start", "START", "Name", "ID"):
        nid = f"json_{variant.lower()}"
        G.add_node(nid, label=variant, source_file="testhelpers/data.json")
        for i in range(15):
            t = f"{nid}_t{i}"
            G.add_node(t, label=f"X{i}", source_file="testhelpers/data.json")
            G.add_edge(t, nid)
    result = god_nodes(G, top_n=10)
    labels = [r["label"] for r in result]
    for variant in ("Start", "START", "Name", "ID"):
        assert variant not in labels, f"`{variant}` should be filtered as JSON-key noise"