软件过程与管理实验 石家庄铁道大学信息学院
实验4：自动化代码审查
本次实验内容是个人软件过程部分，通过本次实验，学生将掌握以下内容：
1、学会利用DeepSeek等大模型进行代码审查。
2、学会利用 SonarQube，Checkstyle，Review Board等代码自动审查工具。
[实验任务一]：利用DeepSeek等大模型进行代码审查
实验要求：
1. 将自己写得截止目前代码行最多的源代码文件上传DeepSeek、豆包、文心一言等大语言模型，尝试以下两类提示词“请帮我审查上述代码，看看存在哪些问题”和“请帮我看看以上的代码是否遵循了编码规范，并提出建议”的反馈。
2. 尝试的大模型至少2个以上，将大模型回答的截图放到本实验报告中（至少4个回答），并提交源代码的压缩文件包以及按照提示修改后的代码压缩包。
3. 由于每人的代码都不一样，所以本题不会出现雷同回答，如果出现雷同一律0分（切勿将本人答案转发他人，更勿抄袭他人，其它左右也一样）。
Deepseek
Kimi
[实验任务二]：利用SonarQube进行代码审查
实验要求：
- 安装SonarQube，并对任务一中的代码进行审查，查看其中的问题，附上SonarQube的报告截图（类似下图）。
- 修改代码
import streamlit as st
import base64
import requests
from PIL import Image
import io
import time
# 百度智慧云API配置
class BaiduAPIConfig:
API_KEY = 'DnE8FI0luW1SigJ53wqyVd8I'
SECRET_KEY = 'fDUfEDmeJk5MVhCfcRmOW0pmVfw1f35e'
TOKEN_URL = "https://aip.baidubce.com/oauth/2.0/token"
API_BASE_URL = "https://aip.baidubce.com/rest/2.0/image-process/v1/"
# 缓存Access Token
@st.cache_data(ttl=25000) # 缓存1小时左右，接近百度Token有效期
def get_access_token():
params = {
"grant_type": "client_credentials",
"client_id": BaiduAPIConfig.API_KEY,
"client_secret": BaiduAPIConfig.SECRET_KEY
}
try:
response = requests.get(BaiduAPIConfig.TOKEN_URL, params=params, timeout=10)
response.raise_for_status()
return response.json().get('access_token')
except requests.exceptions.RequestException as e:
st.error(f"获取Token失败: {str(e)}")
return None
# 图像处理服务
class ImageProcessingService:
def __init__(self, access_token):
self.access_token = access_token
self.headers = {'Content-Type': 'application/json'}
def _send_request(self, endpoint, params, operation_name):
with st.spinner(f"正在执行{operation_name}..."):
start_time = time.time()
url = f"{BaiduAPIConfig.API_BASE_URL}{endpoint}?access_token={self.access_token}"
try:
response = requests.post(url, headers=self.headers, json=params, timeout=30)
response.raise_for_status()
elapsed_time = time.time() - start_time
st.success(f"{operation_name}完成，耗时{elapsed_time:.2f}秒")
return response.json()
except requests.exceptions.RequestException as e:
st.error(f"{operation_name}失败: {str(e)}")
return None
def repair_image(self, image_base64):
params = {
"image": image_base64,
"rectangle": self._get_image_rectangle(image_base64)
}
return self._send_request("inpainting", params, "图像修复")
def cartoonize(self, image_base64):
params = {"image": image_base64}
return self._send_request("selfie_anime", params, "人物动漫化")
def enhance(self, image_base64):
params = {"image": image_base64}
return self._send_request("image_definition_enhance", params, "图像清晰度增强")
def style_transfer(self, image_base64, style):
params = {"image": image_base64, "option": style}
return self._send_request("style_trans", params, f"图像{style}风格转换")
def stretch_restore(self, image_base64):
params = {"image": image_base64}
return self._send_request("stretch_restore", params, "拉伸图像恢复")
def _get_image_rectangle(self, image_base64):
try:
img_bytes = base64.b64decode(image_base64)
img = Image.open(io.BytesIO(img_bytes))
width, height = img.size
return f"0,0,{width},{height}"
except Exception:
return "0,0,100,100" # 默认值，防止异常
# 工具类
class ImageUtils:
@staticmethod
def image_to_base64(image):
buffered = io.BytesIO()
image.save(buffered, format="JPEG")
return base64.b64encode(buffered.getvalue()).decode('utf-8')
@staticmethod
def base64_to_image(encoded_string):
try:
decoded_bytes = base64.b64decode(encoded_string)
return Image.open(io.BytesIO(decoded_bytes))
except Exception as e:
st.error(f"图像解码失败: {str(e)}")
return None
# 应用布局
class AppLayout:
def __init__(self):
self.operations = {
"图像修复": self.process_repair,
"人物动漫化": self.process_cartoonize,
"图像清晰度增强": self.process_enhance,
"图像风格转换": self.process_style,
"拉伸图像恢复": self.process_stretch
}
def render_sidebar(self):
st.sidebar.title("照片修复程序")
st.sidebar.markdown("---")
st.sidebar.info("本应用使用百度AI开放平台API进行图像处理")
st.sidebar.markdown("---")
st.sidebar.write("支持的图像处理功能：")
for op in self.operations.keys():
st.sidebar.write(f"- {op}")
def process_repair(self, service, image_base64):
return service.repair_image(image_base64)
def process_cartoonize(self, service, image_base64):
return service.cartoonize(image_base64)
def process_enhance(self, service, image_base64):
return service.enhance(image_base64)
def process_style(self, service, image_base64):
styles = ["cartoon", "watercolor", "oil", "sketch", "pencil", "colored_pencil"]
style = st.selectbox("选择风格", styles)
return service.style_transfer(image_base64, style)
def process_stretch(self, service, image_base64):
return service.stretch_restore(image_base64)
# 主应用
def main():
st.set_page_config(
page_title="照片修复程序",
page_icon="��️",
layout="wide"
)
app_layout = AppLayout()
app_layout.render_sidebar()
st.title("��️ 照片修复程序")
st.markdown("上传一张图片并选择处理方式，体验AI图像处理效果")
# 获取Access Token
access_token = get_access_token()
if not access_token:
st.error("无法获取API访问凭证，请检查配置")
return
# 上传区域
uploaded_file = st.file_uploader(
"选择图片",
type=["jpg", "jpeg", "png"],
help="支持JPG、PNG格式的图片文件"
)
if uploaded_file is None:
st.info("请上传一张图片开始处理")
return
# 处理上传的图片
original_image = Image.open(uploaded_file)
# 显示原始图片
col1, col2 = st.columns(2)
with col1:
st.subheader("原始图片")
st.image(original_image, use_column_width=True)
img_width, img_height = original_image.size
st.write(f"尺寸: {img_width}x{img_height}px")
# 选择处理类型
with col2:
st.subheader("处理选项")
operation = st.selectbox(
"选择处理类型",
list(app_layout.operations.keys())
)
if st.button("开始处理", type="primary"):
service = ImageProcessingService(access_token)
image_base64 = ImageUtils.image_to_base64(original_image)
# 执行处理
result = app_layout.operations[operation](service, image_base64)
# 显示结果
if result and 'image' in result:
with col2:
st.subheader(f"{operation}结果")
processed_image = ImageUtils.base64_to_image(result['image'])
if processed_image:
st.image(processed_image, use_column_width=True)
# 下载按钮
buffered = io.BytesIO()
processed_image.save(buffered, format="JPEG")
img_bytes = buffered.getvalue()
st.download_button(
label="下载处理后的图片",
data=img_bytes,
file_name=f"processed_{operation}.jpg",
mime="image/jpeg"
)
elif result:
st.json(result)
else:
st.error("处理失败，请重试")
if __name__ == "__main__":
main()
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