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"""
代码嵌入引擎测试
Author: ModuleMirror
"""
import math
from gh_similarity_detector.core.similarity.embedding import (
CodeEmbedding,
DummyEngine,
Code2VecEngine,
create_embedding_engine,
compute_semantic_similarity,
)
class TestCodeEmbedding:
def test_cosine_similarity_identical(self):
e = CodeEmbedding(code_id="a", vector=[1.0, 0.0, 0.0], model_name="test", dimension=3)
assert abs(e.cosine_similarity(e) - 1.0) < 0.01
def test_cosine_similarity_orthogonal(self):
e1 = CodeEmbedding(code_id="a", vector=[1.0, 0.0], model_name="test", dimension=2)
e2 = CodeEmbedding(code_id="b", vector=[0.0, 1.0], model_name="test", dimension=2)
assert abs(e1.cosine_similarity(e2)) < 0.01
def test_cosine_similarity_opposite(self):
e1 = CodeEmbedding(code_id="a", vector=[1.0, 0.0], model_name="test", dimension=2)
e2 = CodeEmbedding(code_id="b", vector=[-1.0, 0.0], model_name="test", dimension=2)
assert abs(e1.cosine_similarity(e2) + 1.0) < 0.01
def test_cosine_similarity_zero_vector(self):
e1 = CodeEmbedding(code_id="a", vector=[0.0, 0.0], model_name="test", dimension=2)
e2 = CodeEmbedding(code_id="b", vector=[1.0, 0.0], model_name="test", dimension=2)
assert e1.cosine_similarity(e2) == 0.0
def test_cosine_similarity_dimension_mismatch(self):
e1 = CodeEmbedding(code_id="a", vector=[1.0], model_name="test", dimension=1)
e2 = CodeEmbedding(code_id="b", vector=[1.0, 0.0], model_name="test", dimension=2)
assert e1.cosine_similarity(e2) == 0.0
def test_euclidean_distance(self):
e1 = CodeEmbedding(code_id="a", vector=[0.0, 0.0], model_name="test", dimension=2)
e2 = CodeEmbedding(code_id="b", vector=[3.0, 4.0], model_name="test", dimension=2)
assert abs(e1.euclidean_distance(e2) - 5.0) < 0.01
def test_to_dict(self):
e = CodeEmbedding(code_id="a", vector=[1.0] * 20, model_name="test", dimension=20)
d = e.to_dict()
assert len(d["vector"]) == 10
assert d["dimension"] == 20
class TestDummyEngine:
def test_embed(self):
engine = DummyEngine()
emb = engine.embed("def hello(): pass")
assert emb.model_name == "dummy"
assert emb.dimension == 16
assert len(emb.vector) == 16
def test_embed_deterministic(self):
engine = DummyEngine()
e1 = engine.embed("code")
e2 = engine.embed("code")
assert e1.vector == e2.vector
def test_embed_different_code(self):
engine = DummyEngine()
e1 = engine.embed("code_a")
e2 = engine.embed("code_b")
assert e1.vector != e2.vector
def test_embed_batch(self):
engine = DummyEngine()
codes = {"a.py": "code_a", "b.py": "code_b"}
results = engine.embed_batch(codes)
assert len(results) == 2
def test_model_name(self):
assert DummyEngine().model_name() == "dummy"
def test_dimension(self):
assert DummyEngine().dimension() == 16
class TestCode2VecEngine:
def test_embed(self):
engine = Code2VecEngine(dimension=64)
emb = engine.embed("def hello():\n print('hello')\n")
assert emb.model_name == "code2vec"
assert emb.dimension == 64
assert len(emb.vector) == 64
def test_embed_normalized(self):
engine = Code2VecEngine(dimension=32)
emb = engine.embed("x = 1\ny = 2\n")
norm = math.sqrt(sum(v * v for v in emb.vector))
if emb.metadata.get("num_paths", 0) > 0:
assert abs(norm - 1.0) < 0.01
def test_embed_empty(self):
engine = Code2VecEngine(dimension=32)
emb = engine.embed("")
assert len(emb.vector) == 32
def test_similar_code_related(self):
engine = Code2VecEngine(dimension=64)
e1 = engine.embed("def add(a, b): return a + b", "add")
e2 = engine.embed("def add(x, y): return x + y", "add2")
sim = e1.cosine_similarity(e2)
assert abs(sim) > 0.3
def test_embed_batch(self):
engine = Code2VecEngine(dimension=32)
codes = {"a.py": "def a(): pass", "b.py": "def b(): pass"}
results = engine.embed_batch(codes)
assert len(results) == 2
def test_custom_dimension(self):
engine = Code2VecEngine(dimension=256)
emb = engine.embed("code")
assert emb.dimension == 256
assert len(emb.vector) == 256
def test_metadata(self):
engine = Code2VecEngine()
emb = engine.embed("def f(): pass")
assert "num_paths" in emb.metadata
class TestCreateEmbeddingEngine:
def test_dummy(self):
engine = create_embedding_engine("dummy")
assert isinstance(engine, DummyEngine)
def test_code2vec(self):
engine = create_embedding_engine("code2vec")
assert isinstance(engine, Code2VecEngine)
def test_unknown(self):
try:
create_embedding_engine("nonexistent")
assert False
except ValueError:
pass
class TestComputeSemanticSimilarity:
def test_compute(self):
engine = Code2VecEngine(dimension=32)
embs_a = [engine.embed("def a(): return 1", "a")]
embs_b = [engine.embed("def b(): return 2", "b")]
results = compute_semantic_similarity(embs_a, embs_b)
assert len(results) == 1
assert "semantic_similarity" in results[0]
assert "euclidean_distance" in results[0]
def test_cross_model_skipped(self):
dummy = DummyEngine()
c2v = Code2VecEngine(dimension=16)
embs_a = [dummy.embed("code", "a")]
embs_b = [c2v.embed("code", "b")]
results = compute_semantic_similarity(embs_a, embs_b)
assert len(results) == 0