633 lines
16 KiB
Markdown
633 lines
16 KiB
Markdown
---
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tags:
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aliases:
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date: 2025-08-04
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time: 16:49:36
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description:
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---
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# 核心設計原則
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遵循3Rs架構：
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1. 可讀性（Readability）：讓程式碼易於理解和維護
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2. 可重用性（Reusability）：減少重複程式碼，提高開發效率
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3. 可重構性（Refactorability）：支援模組化設計和持續改進
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# 第一階段：基礎理論與核心原則
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- **Clean Code定義與重要性**
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- 什麼是Clean Code：易讀、易懂、易維護的程式碼
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- 技術債務的概念與影響
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- 童子軍規則：讓程式碼比接手時更乾淨
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Good code:
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```python
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def calculate_doubled_value_plus_one(input_value):
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"""計算輸入值的兩倍加一"""
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OFFSET = 1
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if input_value <= 0:
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raise ValueError("Input value must be positive")
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doubled_value = input_value * 2
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result = doubled_value + OFFSET
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return result
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```
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Bad code:
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```python
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def f(x):
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if x > 0:
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y = x * 2
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# TODO: fix this later
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z = y + 1 # magic number
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return z
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else:
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return -1
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```
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- **有意義的命名規範**
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- 使用具描述性且明確的變數名稱
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- 函式命名：動詞片語表達動作意圖
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- 類別命名：名詞表達實體概念
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- 避免縮寫和神秘數字
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Good code:
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```javascript
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// 良好的命名
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let elapsedTimeInDays;
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let activeUsers = [];
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let calculateTotalPrice = (price, quantity) => price * quantity;
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```
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```python
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def fetch_user_profile():
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return user_profile_data
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def validate_user_credentials():
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# 驗證使用者憑證
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codes.....
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```
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Bad code:
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```javascript
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// 糟糕的命名
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let d; // 經過的天數
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let u = []; // 使用者列表
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let calc = (a, b) => a * b;
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```
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```python
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def data():
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return user_info
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def process():
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# 處理某些東西
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pass
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```
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- **函式設計原則**
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- 單一職責原則：一個函式只做一件事
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- 函式長度控制：目標在20行以下
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- 參數管理：最多3個參數的建議
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- 避免副作用與Flag參數
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## 一個函式只做一件事
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Good code:
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```python
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# 遵循單一職責的函式
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def validate_user_data(user_data):
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"""僅負責驗證使用者資料"""
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if not user_data.get('email'):
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raise ValueError("Email required")
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return True
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def save_user_to_database(user_data):
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"""僅負責儲存資料"""
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database.save(user_data)
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def send_welcome_notification(email):
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"""僅負責發送歡迎郵件"""
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email_service.send_welcome_email(email)
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def log_user_registration(username):
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"""僅負責記錄日誌"""
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logger.info(f"User {username} registered successfully")
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```
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Bad code:
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```python
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# 違反單一職責的函式
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def process_user_data(user_data):
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# 驗證資料
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if not user_data.get('email'):
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raise ValueError("Email required")
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# 儲存到資料庫
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database.save(user_data)
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# 發送通知郵件
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email_service.send_welcome_email(user_data['email'])
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# 記錄日誌
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logger.info(f"User {user_data['name']} processed")
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```
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## 避免副作用與Flag參數
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Good code:
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```python
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# 無副作用、單一職責的函式
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def transform_data(data):
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"""純函式：只負責資料轉換"""
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return processed_data
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def save_processed_data(data):
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"""專門負責儲存資料"""
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save_to_disk(data)
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def send_processing_notification():
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"""專門負責發送通知"""
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send_notification_email()
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def update_processing_count():
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"""專門負責更新計數"""
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global last_processed_count
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last_processed_count += 1
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```
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Bad code:
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```python
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# 帶有副作用和flag參數的糟糕函式
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def process_data(data, save_to_file=False, send_email=False):
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# 處理資料
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processed_data = transform_data(data)
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# 副作用：修改全域變數
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global last_processed_count
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last_processed_count += 1
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# Flag參數導致函式做多件事
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if save_to_file:
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save_to_disk(processed_data)
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if send_email:
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send_notification_email()
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return processed_data
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```
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- **程式碼組織結構**
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- 降層原則：由上到下的閱讀順序
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- 錯誤處理：使用例外處理取代錯誤碼
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- 這點值得商榷，業界也爭論不休，不用硬性規定，應該依實際情況決定。
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## 降層原則：由上到下的閱讀順序
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Good code:
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```python
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# 遵循降層原則的順序
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def main_process():
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"""主要處理流程"""
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setup_environment()
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result = core_logic()
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cleanup()
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return result
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def setup_environment():
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"""設定處理環境"""
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# 設定環境
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pass
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def core_logic():
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"""核心邏輯處理"""
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return helper_function()
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def helper_function():
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"""輔助函式"""
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return "helper"
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def cleanup():
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"""清理資源"""
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# 清理
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pass
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```
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Bad code:
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```python
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# 混亂的函式順序
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def helper_function():
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return "helper"
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def main_process():
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setup_environment()
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result = core_logic()
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cleanup()
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return result
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def setup_environment():
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# 設定環境
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pass
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def core_logic():
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return helper_function()
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def cleanup():
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# 清理
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pass
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```
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## 錯誤處理：使用例外處理取代錯誤碼
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這點值得商榷，業界也爭論不休，不用硬性規定，應該依實際情況決定。
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錯誤碼針對已知、能預測的錯誤，而例外則傾向於「不應該、不可能」的錯誤。
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Good code:
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```c
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# 使用例外處理
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def process_file(filename):
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"""處理檔案，使用例外處理錯誤"""
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try:
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with open(filename, 'r') as file:
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content = file.read()
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return process_content(content)
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except FileNotFoundError:
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raise FileProcessingError(f"檔案 {filename} 不存在")
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except PermissionError:
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raise FileProcessingError(f"沒有權限讀取檔案 {filename}")
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except Exception as e:
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raise FileProcessingError(f"處理檔案時發生錯誤: {str(e)}")
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```
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Bad code:
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```c
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// 使用錯誤碼的方式
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int process_file(const char* filename) {
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FILE* file = fopen(filename, "r");
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if (file == NULL) {
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return -1; // 錯誤碼
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}
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char buffer[100];
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if (fread(buffer, 1, sizeof(buffer), file) == 0) {
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fclose(file);
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return -2; // 另一個錯誤碼
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}
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fclose(file);
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return 0; // 成功
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}
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```
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- **註解最佳實踐**
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- 何時需要註解，何時不需要
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- 讓程式碼自我說明
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- 有效註解的撰寫技巧
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- **程式碼格式化**
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- 一致性的重要性
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- 縮排與空白字元的運用
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- 程式碼區塊的邏輯分組
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# 第二階段：進階設計原則
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- **五大SOLID原則詳解**
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- **S**：單一職責原則（SRP）
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- **O**：開放封閉原則（OCP）
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- **L**：里氏替換原則（LSP）
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- **I**：介面隔離原則（ISP）
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- **D**：依賴反轉原則（DIP）
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## 單一職責原則（SRP）
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Good code:
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```python
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class User:
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def __init__(self, username, email):
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self.username = username
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self.email = email
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class UserRepository:
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def save(self, user):
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database.save(user)
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class EmailService:
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def send_email(self, email, message):
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email_service.send(email, message)
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class UserReportGenerator:
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def generate_report(self, user):
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return f"User: {user.username}, Email: {user.email}"
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```
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Bad code:
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```python
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class User:
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def __init__(self, username, email):
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self.username = username
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self.email = email
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def save_to_database(self):
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# 儲存使用者到資料庫
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database.save(self)
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def send_email(self, message):
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# 發送郵件
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email_service.send(self.email, message)
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def generate_report(self):
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# 產生使用者報告
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return f"User: {self.username}, Email: {self.email}"
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```
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## 開放封閉原則（OCP）
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Good code:
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```python
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from abc import ABC, abstractmethod
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class DiscountStrategy(ABC):
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@abstractmethod
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def calculate_discount(self, amount):
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pass
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class RegularCustomerDiscount(DiscountStrategy):
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def calculate_discount(self, amount):
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return amount * 0.05
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class PremiumCustomerDiscount(DiscountStrategy):
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def calculate_discount(self, amount):
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return amount * 0.10
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class VIPCustomerDiscount(DiscountStrategy):
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def calculate_discount(self, amount):
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return amount * 0.15
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class DiscountCalculator:
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def __init__(self, strategy: DiscountStrategy):
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self.strategy = strategy
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def calculate_discount(self, amount):
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return self.strategy.calculate_discount(amount)
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```
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Bad code:
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```python
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class DiscountCalculator:
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def calculate_discount(self, customer_type, amount):
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if customer_type == "regular":
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return amount * 0.05
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elif customer_type == "premium":
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return amount * 0.10
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elif customer_type == "vip":
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return amount * 0.15
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else:
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return 0
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```
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- **實務應用案例**
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- 每個原則的程式碼範例
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- 違反原則的常見問題
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- **常用設計模式**
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- 工廠模式：封裝物件創建
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- 策略模式：演算法替換
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- 觀察者模式：事件處理
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- 裝飾者模式：功能擴展
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- 還有其他的設計模式，這裡不一一列出。設計模式可以給大家一個共同的語言，溝通可以更清楚、方便。
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## 工廠模式：封裝物件創建
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Good code:
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```python
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from abc import ABC, abstractmethod
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class Transport(ABC):
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@abstractmethod
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def deliver(self):
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pass
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class Car(Transport):
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def deliver(self):
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return "Delivering by car"
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class Bike(Transport):
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def deliver(self):
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return "Delivering by bike"
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class TransportFactory:
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_transports = {
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"car": Car,
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"bike": Bike
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}
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@classmethod
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def create_transport(cls, transport_type):
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transport_class = cls._transports.get(transport_type)
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if not transport_class:
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raise ValueError(f"Unknown transport type: {transport_type}")
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return transport_class()
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```
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Bad code:
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```python
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# 直接創建物件，違反開放封閉原則
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def create_transport(transport_type):
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if transport_type == "car":
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return Car()
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elif transport_type == "bike":
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return Bike()
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elif transport_type == "plane":
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return Plane()
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else:
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raise ValueError("Unknown transport type")
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```
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## 策略模式：演算法替換
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Good code:
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```python
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class PaymentStrategy(ABC):
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@abstractmethod
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def process_payment(self, amount):
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pass
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class CreditCardPayment(PaymentStrategy):
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def process_payment(self, amount):
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return f"Processing ${amount} via credit card"
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class PayPalPayment(PaymentStrategy):
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def process_payment(self, amount):
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return f"Processing ${amount} via PayPal"
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class PaymentProcessor:
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def __init__(self, strategy: PaymentStrategy):
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self.strategy = strategy
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def set_strategy(self, strategy: PaymentStrategy):
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self.strategy = strategy
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def process_payment(self, amount):
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return self.strategy.process_payment(amount)
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```
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Bad code:
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```python
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class PaymentProcessor:
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def process_payment(self, amount, payment_type):
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if payment_type == "credit_card":
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# 信用卡處理邏輯
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return self._process_credit_card(amount)
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elif payment_type == "paypal":
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# PayPal處理邏輯
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return self._process_paypal(amount)
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elif payment_type == "bank_transfer":
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# 銀行轉帳處理邏輯
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return self._process_bank_transfer(amount)
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```
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- **重構基本概念**
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- 重構定義：改善內部結構不影響外部行為
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- 重構時機：三次法則與預備性重構
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- 安全重構：依賴測試確保正確性
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## 重構定義：改善內部結構不影響外部行為
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Good code:
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```python
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# 重構後：清晰且易於維護的程式碼
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def calculate_price(items):
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return sum(calculate_item_price(item) for item in items)
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def calculate_item_price(item):
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base_price = item['price'] * item['quantity']
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discount = get_discount_rate(item)
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return base_price * (1 - discount)
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def get_discount_rate(item):
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discount_rules = {
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'book': lambda qty: 0.1 if qty > 10 else 0,
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'electronics': lambda qty: 0.15 if qty > 5 else 0
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}
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rule = discount_rules.get(item['type'], lambda qty: 0)
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return rule(item['quantity'])
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```
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Bad code:
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```python
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# 重構前：複雜且難以理解的程式碼
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def calculate_price(items):
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total = 0
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for item in items:
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if item['type'] == 'book':
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if item['quantity'] > 10:
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total += item['price'] * item['quantity'] * 0.9
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else:
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total += item['price'] * item['quantity']
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elif item['type'] == 'electronics':
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if item['quantity'] > 5:
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total += item['price'] * item['quantity'] * 0.85
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else:
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total += item['price'] * item['quantity']
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else:
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total += item['price'] * item['quantity']
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return total
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```
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- **常用重構手法**
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- 提取方法（Extract Method）
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- 封裝成員變數（Encapsulate Field）
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- 方法更名（Rename Method）
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- 消除重複程式碼
|
||
|
||
# 第三階段：實戰應用
|
||
- **TDD基本概念與流程**
|
||
- 紅燈-綠燈-重構循環
|
||
- 單元測試的撰寫技巧
|
||
- 測試覆蓋率與品質指標
|
||
|
||
## 紅燈-綠燈-重構循環
|
||
Good code:
|
||
```python
|
||
# 正確的TDD方法：先寫測試
|
||
import pytest
|
||
|
||
# 1. 紅燈：先寫失敗的測試
|
||
def test_factorial_of_zero():
|
||
assert calculate_factorial(0) == 1
|
||
|
||
def test_factorial_of_positive_number():
|
||
assert calculate_factorial(5) == 120
|
||
|
||
def test_factorial_of_negative_number():
|
||
with pytest.raises(ValueError):
|
||
calculate_factorial(-1)
|
||
|
||
# 2. 綠燈：寫最少的程式碼讓測試通過
|
||
def calculate_factorial(n):
|
||
if n < 0:
|
||
raise ValueError("Factorial is not defined for negative numbers")
|
||
if n == 0:
|
||
return 1
|
||
|
||
result = 1
|
||
for i in range(1, n + 1):
|
||
result *= i
|
||
return result
|
||
|
||
# 3. 重構：改善程式碼品質
|
||
def calculate_factorial(n):
|
||
"""計算階乘值"""
|
||
if n < 0:
|
||
raise ValueError("Factorial is not defined for negative numbers")
|
||
|
||
if n <= 1:
|
||
return 1
|
||
|
||
return n * calculate_factorial(n - 1)
|
||
```
|
||
|
||
Bad code:
|
||
```python
|
||
# 錯誤的TDD方法：先寫實作再寫測試
|
||
def calculate_factorial(n):
|
||
if n < 0:
|
||
return None
|
||
if n == 0 or n == 1:
|
||
return 1
|
||
result = 1
|
||
for i in range(2, n + 1):
|
||
result *= i
|
||
return result
|
||
|
||
# 之後才寫測試
|
||
def test_factorial():
|
||
assert calculate_factorial(5) == 120
|
||
assert calculate_factorial(0) == 1
|
||
```
|
||
|
||
- **專案案例分析**
|
||
- BMI計算機Clean Code版本實作
|
||
- 購物車系統的Clean Code改造
|
||
- 登錄註冊系統的重構實戰
|
||
|
||
- **程式碼審查流程**
|
||
- 審查清單建立
|
||
- 給予建設性回饋的技巧
|
||
- 處理團隊中程式碼品質分歧
|
||
|
||
## 審查清單建立
|
||
### Good
|
||
✅ 良好的程式碼審查評論：
|
||
- "這個函式做了太多事情，建議分解成較小的函式以提高可讀性"
|
||
- "考慮使用更描述性的變數名稱，例如將 'data' 改為 'user_profiles'"
|
||
- "這裡可能有空指標例外的風險，建議加上 null 檢查"
|
||
- "很好的重構！這樣的設計更容易測試和維護"
|
||
- "建議加上單元測試來覆蓋這個邊界情況"
|
||
|
||
### Bad
|
||
❌ 糟糕的程式碼審查評論：
|
||
- "你的程式碼很爛"
|
||
- "為什麼要這樣寫？"
|
||
- "這個不對"
|
||
- "重寫整個函式"
|
||
|
||
# 第四階段：進階實踐
|
||
- **效能最佳化考量**
|
||
- 何時最佳化？
|
||
- Clean Code與效能的權衡
|
||
- 常見效能反模式避免
|
||
|
||
- **建立個人程式碼品質標準**
|
||
|
||
# 參考來源 |