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java-sim-pdg-opt.py
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java-sim-pdg-opt.py
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# -*- coding: utf-8 -*-
"""
Program Depende Graph (PDG) 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
import networkx as nx
# Generate Program Dependence Graph (PDG) for Java code snippets
def generate_pdg(code):
tokens = list(javalang.tokenizer.tokenize(code))
parser = javalang.parser.Parser(tokens)
tree = parser.parse_member_declaration()
graph = nx.DiGraph()
for path, node in tree:
if isinstance(node, javalang.tree.MethodDeclaration):
method_name = node.name
graph.add_node(method_name)
# Simulated data dependence
for param in node.parameters:
graph.add_edge(param.name, method_name)
# Simulated control dependence
if isinstance(node.body, javalang.tree.BlockStatement):
for stmt in node.body.statements:
if isinstance(stmt, javalang.tree.IfStatement):
graph.add_edge(stmt.expression.value, method_name)
return graph
# 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 = [0.1, 0.2, 0.3]
# 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
try:
pdg_1 = generate_pdg(code1)
pdg_2 = generate_pdg(code2)
normalized_edit_distance = nx.graph_edit_distance(pdg_1, pdg_2, node_match=lambda n1, n2: n1 == n2) / (len(pdg_1.nodes) + len(pdg_2.nodes))
similarity_ratio = 1 - normalized_edit_distance
except Exception as e:
similarity_ratio = 0
# print(f"{subfolder_name},{similarity_ratio:.2f}")
# 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}")