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java-sim-metrics-opt.py
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java-sim-metrics-opt.py
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# -*- coding: utf-8 -*-
"""
Metrics Similarity Detection for Java Code
Martinez-Gil, J. (2024). Source Code Clone Detection Using Unsupervised Similarity Measures. arXiv preprint arXiv:2401.09885.
@author: Jorge Martinez-Gil
"""
import os
import javalang
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np
def calculate_code_metrics(code):
tree = javalang.parse.parse(code)
code_length = len(code.split('\n'))
# Calculate cyclomatic complexity (you need to implement this)
cyclomatic_complexity = 1
# Count the number of variables
num_variables = sum(1 for _, node in tree.filter(javalang.tree.VariableDeclarator))
# Count the number of functions/methods
num_functions = sum(1 for _, node in tree.filter(javalang.tree.MethodDeclaration))
# Count the number of control statements (e.g., if, for, while)
num_control_statements = sum(1 for _, node in tree.filter(javalang.tree.IfStatement))
num_control_statements += sum(1 for _, node in tree.filter(javalang.tree.ForStatement))
num_control_statements += sum(1 for _, node in tree.filter(javalang.tree.WhileStatement))
num_control_statements += sum(1 for _, node in tree.filter(javalang.tree.DoStatement))
return np.array([code_length, cyclomatic_complexity, num_variables, num_functions, num_control_statements])
def calculate_code_metrics2(code_snippet):
# Count the number of loops (for, while)
num_loops = code_snippet.count("for") + code_snippet.count("while")
# Count the number of conditionals (if, else, switch)
num_conditionals = code_snippet.count("if") + code_snippet.count("else") + code_snippet.count("switch")
# Count the number of function/method calls
num_function_calls = code_snippet.count("(")
# Calculate the depth of nesting using curly braces
brace_depth = 0
max_brace_depth = 0
for char in code_snippet:
if char == "{":
brace_depth += 1
if brace_depth > max_brace_depth:
max_brace_depth = brace_depth
elif char == "}":
brace_depth -= 1
# Create a syntax feature vector
syntax_vector = [num_loops, num_conditionals, num_function_calls, max_brace_depth]
return syntax_vector
# Define the path to the IR-Plag-Dataset folder
dataset_path = os.path.join(os.getcwd(), "IR-Plag-Dataset")
# Define a list of similarity thresholds to iterate over
similarity_thresholds = [0.98]
# Initialize variables to keep track of the best result
best_threshold = 0
best_accuracy = 0
# Initialize counters
TP = 0
FP = 0
FN = 0
# Loop through each similarity threshold and calculate accuracy
for SIMILARITY_THRESHOLD in similarity_thresholds:
# Initialize the counters
total_cases = 0
over_threshold_cases_plagiarized = 0
over_threshold_cases_non_plagiarized = 0
cases_plag = 0
cases_non_plag = 0
# Loop through each subfolder in the dataset
for folder_name in os.listdir(dataset_path):
folder_path = os.path.join(dataset_path, folder_name)
if os.path.isdir(folder_path):
# Find the Java file in the original folder
original_path = os.path.join(folder_path, 'original')
java_files = [f for f in os.listdir(original_path) if f.endswith('.java')]
if len(java_files) == 1:
java_file = java_files[0]
with open(os.path.join(original_path, java_file), 'r') as f:
code1 = f.read()
# print(f"Found {java_file} in {original_path} for {folder_name}")
# Loop through each subfolder in the plagiarized and non-plagiarized folders
for subfolder_name in ['plagiarized', 'non-plagiarized']:
subfolder_path = os.path.join(folder_path, subfolder_name)
if os.path.isdir(subfolder_path):
# Loop through each Java file in the subfolder
for root, dirs, files in os.walk(subfolder_path):
for java_file in files:
if java_file.endswith('.java'):
with open(os.path.join(root, java_file), 'r') as f:
code2 = f.read()
# print(f"Found {java_file} in {root} for {folder_name}")
# Calculate the similarity ratio
metrics_snippet1 = calculate_code_metrics(code1)
metrics_snippet2 = calculate_code_metrics(code2)
# Reshape the metric arrays
metrics_snippet1 = metrics_snippet1.reshape(1, -1)
metrics_snippet2 = metrics_snippet2.reshape(1, -1)
# Calculate cosine similarity between the metric arrays
#csimilarity_ratio = cosine_similarity(metrics_snippet1, metrics_snippet2)[0][0]
similarity_ratio = cosine_similarity(metrics_snippet1, metrics_snippet2)[0][0]
# print(f"{subfolder_name},{similarity_ratio:.6f}")
# Update the counters based on the similarity ratio
if subfolder_name == 'plagiarized':
cases_plag += 1
if similarity_ratio >= SIMILARITY_THRESHOLD:
over_threshold_cases_plagiarized += 1
elif subfolder_name == 'non-plagiarized':
cases_non_plag += 1
if similarity_ratio < SIMILARITY_THRESHOLD:
over_threshold_cases_non_plagiarized += 1
total_cases += 1
# Update the counters based on the similarity ratio
if subfolder_name == 'plagiarized':
cases_plag += 1
if similarity_ratio >= SIMILARITY_THRESHOLD:
TP += 1 # True positive: plagiarized and identified as plagiarized
else:
FN += 1 # False negative: plagiarized but identified as non-plagiarized
elif subfolder_name == 'non-plagiarized':
cases_non_plag += 1
if similarity_ratio <= SIMILARITY_THRESHOLD:
over_threshold_cases_non_plagiarized += 1
else:
FP += 1 # False positive: non-plagiarized but identified as plagiarized
else:
print(f"Error: Found {len(java_files)} Java files in {original_path} for {folder_name}")
# Calculate accuracy for the current threshold
if total_cases > 0:
accuracy = (over_threshold_cases_non_plagiarized + over_threshold_cases_plagiarized) / total_cases
if accuracy > best_accuracy:
best_accuracy = accuracy
best_threshold = SIMILARITY_THRESHOLD
# Calculate precision and recall
if TP + FP > 0:
precision = TP / (TP + FP)
else:
precision = 0
if TP + FN > 0:
recall = TP / (TP + FN)
else:
recall = 0
# Calculate F-measure
if precision + recall > 0:
f_measure = 2 * (precision * recall) / (precision + recall)
else:
f_measure = 0
# Print the best threshold and accuracy
print(f"{os.path.basename(__file__)} - The best threshold is {best_threshold} with an accuracy of {best_accuracy:.2f}, Precision: {precision:.2f}, Recall: {recall:.2f}, F-measure: {f_measure:.2f}")