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functions similartiy thru codebert and diffllib working , function s…
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…tructure sim thru ast left , models sim left
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@krrish-sehgal
krrish-sehgal committed Dec 19, 2024
1 parent 1df23ff commit aa0a36f
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1 change: 1 addition & 0 deletions pyproject.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -84,6 +84,7 @@ slack-bolt = "^1.22.0"
gitpython = "^3.1.43"
transformers = "^4.47.1"
torch = "^2.5.1"
scikit-learn = "^1.6.0"

[tool.poetry.group.dev.dependencies]
black = "^24.8.0"
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190 changes: 92 additions & 98 deletions website/similarity_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,14 +3,16 @@
import os
import re

from transformers import AutoModelForSequenceClassification, AutoTokenizer
from transformers import AutoTokenizer, AutoModel
from sklearn.metrics.pairwise import cosine_similarity
import torch

# Initialize CodeBERT model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("microsoft/codebert-base")
model = AutoModelForSequenceClassification.from_pretrained("microsoft/codebert-base")
model = AutoModel.from_pretrained("microsoft/codebert-base")


def process_similarity_analysis(repo1_path, repo2_path, use_codebert=False):
def process_similarity_analysis(repo1_path, repo2_path):
"""
Process the similarity analysis between two repositories.
:param repo1_path: Path to the first repository
Expand All @@ -24,51 +26,43 @@ def process_similarity_analysis(repo1_path, repo2_path, use_codebert=False):
"models": [],
}

# Step 1: Compare function and model names using difflib or Levenshtein
functions1 = extract_function_names(repo1_path)
functions2 = extract_function_names(repo2_path)

# Compare function names
for f1 in functions1:
for f2 in functions2:
if use_codebert:
similarity = analyze_code_similarity_with_codebert(f1, f2)
else:
similarity = difflib.SequenceMatcher(None, f1, f2).ratio() * 100

if similarity > 80: # You can set the threshold here
matching_details["functions"].append(
{
"name1": f1,
"name2": f2,
"similarity": round(similarity, 2),
}
)

# Step 2: Compare method signatures
method_signatures1 = extract_method_signatures(repo1_path)
method_signatures2 = extract_method_signatures(repo2_path)

# Compare method signatures
for m1 in method_signatures1:
for m2 in method_signatures2:
if use_codebert:
signature1 = f"{m1['name']}({', '.join(m1['args'])})"
signature2 = f"{m2['name']}({', '.join(m2['args'])})"
similarity = analyze_code_similarity_with_codebert(signature1, signature2)
else:
similarity = difflib.SequenceMatcher(None, str(m1), str(m2)).ratio() * 100

if similarity > 80: # You can set the threshold here
matching_details["functions"].append(
{
"method1": str(m1),
"method2": str(m2),
"similarity": round(similarity, 2),
}
)

# Step 3: Compare Django models
# Step 1: Extract function signatures and content
functions1 = extract_function_signatures_and_content(repo1_path)
functions2 = extract_function_signatures_and_content(repo2_path)
print(functions1)
print(functions2)
# Compare functions
for func1 in functions1:
for func2 in functions2:
# Name similarity
name_similarity_difflib = difflib.SequenceMatcher(None, func1["signature"]["name"], func2["signature"]["name"]).ratio() * 100
name_similarity_codebert = analyze_code_similarity_with_codebert(func1["signature"]["name"], func2["signature"]["name"])
name_similarity = (name_similarity_difflib + name_similarity_codebert) / 2
print(f"Name similarity: {name_similarity}")

# Signature similarity
signature1 = f"{func1['signature']['name']}({', '.join(func1['signature']['args'])})"
signature2 = f"{func2['signature']['name']}({', '.join(func2['signature']['args'])})"
signature_similarity_difflib = difflib.SequenceMatcher(None, signature1, signature2).ratio() * 100
signature_similarity_codebert = analyze_code_similarity_with_codebert(signature1, signature2)
signature_similarity = (signature_similarity_difflib + signature_similarity_codebert) / 2
print(f"Signature similarity: {signature_similarity}")

# Content similarity
content_similarity = analyze_code_similarity_with_codebert(func1["full_text"], func2["full_text"])
print(f"Content similarity: {content_similarity}")

# Aggregate similarity
overall_similarity = (name_similarity + signature_similarity + content_similarity) / 3
print(f"Overall similarity: {overall_similarity}")
# if overall_similarity > 80: # You can set the threshold here
matching_details["functions"].append({
"name1": func1["signature"]["name"],
"name2": func2["signature"]["name"],
"similarity": round(overall_similarity, 2),
})

# Step 2: Compare Django models
models1 = extract_django_models(repo1_path)
models2 = extract_django_models(repo2_path)

Expand All @@ -77,14 +71,9 @@ def process_similarity_analysis(repo1_path, repo2_path, use_codebert=False):
# Compare models and fields
for model1 in models1:
for model2 in models2:
if use_codebert:
model_similarity = analyze_code_similarity_with_codebert(
model1["name"], model2["name"]
)
else:
model_similarity = (
difflib.SequenceMatcher(None, model1["name"], model2["name"]).ratio() * 100
)
model_similarity = (
difflib.SequenceMatcher(None, model1["name"], model2["name"]).ratio() * 100
)

if model_similarity > 80: # You can set the threshold here
model_fields_similarity = compare_model_fields(model1, model2)
Expand All @@ -99,64 +88,67 @@ def process_similarity_analysis(repo1_path, repo2_path, use_codebert=False):

return similarity_score, matching_details


def analyze_code_similarity_with_codebert(code1, code2):
"""
Analyze the semantic similarity between two code snippets using CodeBERT.
Analyze the semantic similarity between two code snippets using CodeBERT embeddings.
:param code1: First code snippet
:param code2: Second code snippet
:return: Similarity score (0-100)
"""
inputs = tokenizer(
f"{code1} [SEP] {code2}", return_tensors="pt", truncation=True, max_length=512
)
outputs = model(**inputs)
logits = outputs.logits
similarity_score = logits.softmax(dim=1)[0][1].item() * 100
print(f"Analyzing similarity between:\nCode1: {code1}\nCode2: {code2}")

# Tokenize and encode inputs
inputs_code1 = tokenizer(code1, return_tensors="pt", truncation=True, max_length=512, padding="max_length")
inputs_code2 = tokenizer(code2, return_tensors="pt", truncation=True, max_length=512, padding="max_length")

# Generate embeddings
with torch.no_grad():
outputs_code1 = model(**inputs_code1)
outputs_code2 = model(**inputs_code2)

# Use mean pooling over the last hidden state to get sentence-level embeddings
embedding_code1 = outputs_code1.last_hidden_state.mean(dim=1)
embedding_code2 = outputs_code2.last_hidden_state.mean(dim=1)

# Compute cosine similarity
similarity = cosine_similarity(embedding_code1.numpy(), embedding_code2.numpy())
similarity_score = similarity[0][0] * 100 # Scale similarity to 0-100

print(f"Similarity score: {similarity_score}")
return round(similarity_score, 2)


def extract_function_names(repo_path):
"""
Extract function names from Python files in the given repo.
:param repo_path: Path to the repository
:return: List of function names
"""
function_names = []
for root, dirs, files in os.walk(repo_path):
for file in files:
if file.endswith(".py"):
file_path = os.path.join(root, file)
with open(file_path, "r") as f:
tree = ast.parse(f.read(), filename=file)
for node in ast.walk(tree):
if isinstance(node, ast.FunctionDef):
function_names.append(node.name)
return function_names


def extract_method_signatures(repo_path):
def extract_function_signatures_and_content(repo_path):
"""
Extract method signatures (name, parameters) from Python files.
Extract function signatures (name, parameters) and full text from Python files.
:param repo_path: Path to the repository
:return: List of method signatures
:return: List of function metadata (signature + full text)
"""
method_signatures = []
functions = []
for root, dirs, files in os.walk(repo_path):
for file in files:
if file.endswith(".py"):
file_path = os.path.join(root, file)
with open(file_path, "r") as f:
tree = ast.parse(f.read(), filename=file)
for node in ast.walk(tree):
if isinstance(node, ast.FunctionDef):
signature = {
"name": node.name,
"args": [arg.arg for arg in node.args.args],
"defaults": [ast.dump(default) for default in node.args.defaults],
}
method_signatures.append(signature)
return method_signatures
try:
file_content = f.read()
tree = ast.parse(file_content, filename=file)
for node in ast.walk(tree):
if isinstance(node, ast.FunctionDef):
signature = {
"name": node.name,
"args": [arg.arg for arg in node.args.args],
"defaults": [ast.dump(default) for default in node.args.defaults],
}
# Extract function body as full text
function_text = ast.get_source_segment(file_content, node)
function_data = {
"signature": signature,
"full_text": function_text, # Full text of the function
}
functions.append(function_data)
except Exception as e:
print(f"Error parsing {file_path}: {e}")
return functions


def extract_django_models(repo_path):
Expand Down Expand Up @@ -185,6 +177,8 @@ def extract_django_models(repo_path):

# Look for class definition that inherits from models.Model
if line.startswith("class ") and "models.Model" in line:
if model_name: # Save the previous model if exists
models.append({"name": model_name, "fields": fields})
model_name = line.split("(")[0].replace("class ", "").strip()
inside_model = True
fields = [] # Reset fields when a new model starts
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2 changes: 1 addition & 1 deletion website/views/organization.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1865,7 +1865,7 @@ def post(self, request, *args, **kwargs):

# Step 2: Perform similarity analysis
similarity_score, matching_details = process_similarity_analysis(
repo1_path, repo2_path, True
repo1_path, repo2_path
)

except ValueError as e:
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