File size: 3,248 Bytes
1da285f | 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 | import os
import pandas as pd
from matplotlib import pyplot as plt, ticker
split = os.getenv('split', '')
suf_split = f'-{split}' if split else ''
RESULT_DIR = f'./results{suf_split}'
NAMES = {
# 'Orienter (GPT-4v)': f'{RESULT_DIR}/aaa_icse_test_set_merged_gpt4v_gpt4v_ape_d_object_bbox_r1i_bf.csv',
# 'Orienter (Claude)': f'{RESULT_DIR}/realfse_union3_claude35sonnet_ape_d_object_bbox_gpu0123_i_bf.csv',
'Orienter (Gemini)': f'{RESULT_DIR}/realfse_union3_gemini15pro_ape_d_object_bbox_gpu0123_i_bf.csv',
# 'Orienter (GPT-4o)': f'{RESULT_DIR}/realfse_union3_gpt4v_ape_d_object_bbox_gpu0123_i_bf.csv',
'YOLOv8': f'{RESULT_DIR}/YOLO.csv',
'Claude-4.5-sonnet': f'{RESULT_DIR}/Claude4_5-sonnet-E2E.csv',
'InternVL 3.5': f'{RESULT_DIR}/internVL-E2E.csv',
# 'GPT-4V-E2E': f'{RESULT_DIR}/GPT4V-E2E.csv',
'Random-based Fuzzing': f'{RESULT_DIR}/random.csv',
}
MARKERS = {
# 'Orienter (GPT-4v)': 'o',
# 'Orienter (Claude)': 's',
'Orienter (Gemini)': 'D',
# 'Orienter (GPT-4o)': '^',
'YOLOv8': '*',
'Claude-4.5-sonnet': 'P',
'InternVL 3.5': 'v',
# 'GPT-4V-E2E': 'v',
'Random-based Fuzzing': 'x',
}
def format_func(x, pos):
return format(int(x), ',').replace(',', ' ')
def plot_effective_interacts_cnt(data):
plt.figure()
ax = plt.gca()
plt.gcf().set_size_inches(8, 6)
ax.set_xlabel('Time/min')
ax.xaxis.set_major_locator(ticker.MultipleLocator(10))
ax.set_ylabel('Effective Interactions')
ax.yaxis.set_major_locator(ticker.MultipleLocator(1000))
ax.yaxis.set_major_formatter(ticker.FuncFormatter(format_func))
for name, df in data.items():
plt.plot(df.index, df['effective_interacts_cnt'], label=name, marker=MARKERS[name], markevery=10, linestyle='-' if name.startswith('Orienter') else '-', markersize=12)
# Add legend
# plt.legend(loc='upper center', bbox_to_anchor=(0.5, -0.15), ncol=2, frameon=False)
# set the size of the fig
plt.savefig(f'{RESULT_DIR}/test-effective-interacts-cnt.png', dpi=300, bbox_inches='tight')
plt.savefig(f'{RESULT_DIR}/test-effective-interacts-cnt.pdf', dpi=300, bbox_inches='tight', format='pdf')
def plot_coverage_rate(data):
plt.figure()
ax = plt.gca()
plt.gcf().set_size_inches(8, 6)
ax.set_xlabel('Time/min')
ax.xaxis.set_major_locator(ticker.MultipleLocator(10))
ax.set_ylabel('IGE Coverage')
ax.yaxis.set_major_locator(ticker.MultipleLocator(0.1))
for name, df in data.items():
plt.plot(df.index, df['coverage_rate'], label=name, marker=MARKERS[name], markevery=10, linestyle='-' if name.startswith('Orienter') else '-', markersize=12)
# Add legend inside the plot
plt.legend(loc='lower right', ncol=1, frameon=False, fontsize=19)
plt.savefig(f'{RESULT_DIR}/test-coverage-rate.png', dpi=300, bbox_inches='tight')
plt.savefig(f'{RESULT_DIR}/test-coverage-rate.pdf', dpi=300, bbox_inches='tight', format='pdf')
def main():
plt.rcParams['font.size'] = 28
# Load the data
data = {}
for name, path in NAMES.items():
data[name] = pd.read_csv(path)
data[name] = data[name][1:]
plot_effective_interacts_cnt(data)
plot_coverage_rate(data)
if __name__ == '__main__':
main()
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