-
Notifications
You must be signed in to change notification settings - Fork 3
/
java-sim-exec-opt.py
166 lines (137 loc) · 6.73 KB
/
java-sim-exec-opt.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
# -*- coding: utf-8 -*-
"""
Output Analysis 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 subprocess
import os
import sys
import time
from difflib import SequenceMatcher
import re
# Compile and execute Java code
def execute_java_code(code, input_data):
# print(code)
pattern = r'class\s+(\w+)\s*{'
# Search for the pattern in the Java code
try:
match = re.search(pattern, code)
class_name = match.group(1)
except:
return 999
java_filename = class_name + ".java"
class_filename = class_name + ".class"
with open(java_filename, "w") as java_file:
java_file.write(code)
subprocess.run(["javac", java_filename], check=True)
process = subprocess.Popen(
["java", class_name],
stdin=subprocess.PIPE,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
text=True
)
stdout, stderr = process.communicate(input=input_data)
os.remove(java_filename) # Clean up the temporary Java file
os.remove(class_filename) # Clean up the temporary Java file
return stdout.strip()
# 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 = [0.941]
# 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}")
input_data = ""
output1 = execute_java_code(code1, input_data)
output2 = execute_java_code(code2, input_data)
# Calculate the similarity ratio
try:
similarity_ratio = SequenceMatcher(None, output1, output2).ratio()
except:
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}")