python基礎教程（第三版)學習筆記（十六）
阿新 • • 發佈：2018-11-24
第十六章 測試基礎
除錯是程式設計師躲不開的宿命，是程式設計工作的有機組成部分。 本章介紹測試的基本知識。培養如何養成在程式設計中進行測試的習慣，並介紹一些可幫 助編寫測試的工具。
16.1 先測試再編碼
要避免程式碼在開發途中被淘汰，必須能夠應對變化並具備一定的靈活性，因此為程式的各個 部分編寫測試至關重要（這稱為單元測試），而且是應用程式設計工作的重要組成部分。極限編 程先鋒引入了“測試一點點，再編寫一點點程式碼”的理念。換而言之，測試在先，編碼在後。這也稱為測試驅動的程式設計。
16.1.1 準確的需求說明
開發軟體時，必須先知道軟體要解決什麼問題——要實現什麼樣的目標。要闡明程式的目標，可編寫需求說明，也就是描述程式必須滿足何種需求的文件（或便條）。 測試程式可以包含在程式內如：
#計算矩形面積
def rect_area(heigth,width): #程式中被呼叫的函式
return heigth*heigth #函式中有一處錯誤
height = 3
width = 4
correct_answer = 12 #設定執行結果
answer = rect_area(height, width)
if answer == correct_answer: #對執行結果進行判斷
print('程式測試通過 ')
else:
print('程式中存在錯誤！')
執行結果：
C:\Users\zhx\AppData\Local\Programs\Python\Python37\python.exe E:/pythonProjects/twisted/finger.py 程式中存在錯誤！ Process finished with exit code 0
16.1.2 做好應對變化的準備
自動化測試不僅可在你編寫程式時提供極大的幫助，還有助於在你修改程式碼時避免累積錯 誤，這在程式規模很大時尤其重要。
16.1.3 測試四步曲
(1) 確定需要實現的新功能。可將其記錄下來，再為之編寫一個測試。
(2) 編寫實現功能的框架程式碼，讓程式能夠執行（不存在語法錯誤之類的問題），但測試依然 無法通過。測試失敗是很重要的，因為這樣你才能確定它可能失敗。 不斷重複這 個過程：確定測試失敗後，再試圖讓它成功。
(3) 編寫讓測試剛好能夠通過的程式碼。
(4) 改進（重構）程式碼以全面而準確地實現所需的功能，同時確保測試依然能夠成功。
16.2 測試工具
有兩個傑出的模組可替你自動完成測試過程。
一是unittest：一個通用的測試框架。
一是 doctest：一個更簡單的模組，是為檢查文件而設計的，但也非常適合用來編寫單元測試。
16.2.1 doctest
使用doctest非常方便，只要在模組最後新增如下程式碼即可：
#... ...以上是模組主體
if __name__=='__main__':
import doctest,模組名
doctest.testmod(模組名)
16.2.2 unittest
雖然doctest使用起來很容易，但unittest（基於流行的Java測試框架JUnit）更靈活、更強大。
unittest的使用和doctest類似：
import json
import unittest
from area import area
f = open('illinois.json', 'r')
illinois = json.loads(f.read())
world = {
'type': 'Polygon',
'coordinates': [
[
[-180, -90],
[-180, 90],
[180, 90],
[180, -90],
[-180, -90]
]
]
}
illinois_area = 145978332359.36746
world_area = 511207893395811.06
class AreaTestCase(unittest.TestCase):
def test_area_illinois_with_string(self):
""" illinois.json檔案輸入計算機 """
# 回到檔案的頂端
f.seek(0)
illinois = f.read()
self.assertEqual(round(area(illinois), 2), round(illinois_area, 2))
def test_area_illinois(self):
""" Compute the area of illinois """
self.assertEqual(round(area(illinois), 2), round(illinois_area, 2))
def test_area_world(self):
""" Compute the area of the whole world """
self.assertEqual(area(world), world_area)
def test_point_area(self):
""" Compute the area of a point """
self.assertEqual(area({'type': 'Point', 'coordinates': [0, 0]}), 0)
def test_liststring_area(self):
""" Compute the area of a line string """
self.assertEqual(area({'type': 'LineString', 'coordinates': [[0, 0], [1, 1]]}), 0)
def test_geometry_collection_area(self):
""" Compute the area of a geometry collection """
total = illinois_area + world_area
self.assertEqual(area({'type': 'GeometryCollection', 'geometries': [world, illinois]}), total)
if __name__ == '__main__':
unittest.main()
函式unittest.main負責替你執行測試：例項化所有的TestCase子類，並執行所有名稱以test 打頭的方法 。
執行結果：
C:\Users\xx\AppData\Local\Programs\Python\Python37\python.exe E:/pythonProjects/twisted/finger.py
......
----------------------------------------------------------------------
Ran 6 tests in 0.004s
OK
Process finished with exit code 0
其中......代表六個函式通過測試，如果不能通過測試會顯示F。
附 illinois.json檔案：
{
"type": "MultiPolygon",
"coordinates": [[
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-87.913651,
38.302345
],
[
-87.925919,
38.304771
],
[
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38.241085
],
[
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38.234814
],
[
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38.200714
],
[
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38.171131
],
[
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38.157528
],
[
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38.136913
],
[
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38.13176
],
[
-88.018547,
38.103302
],
[
-88.012329,
38.092346
],
[
-87.964867,
38.096748
],
[
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38.073307
],
[
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38.054085
],
[
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38.04512
],
[
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38.038303
],
[
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38.033531
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[
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[
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37.975056
],
[
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37.956264
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[
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37.934498
],
[
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37.929783
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[
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37.944
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[
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[
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[
-88.071564,
37.51099
]
]
]]
}
16.3 超越單元測試
要探索程式，還有其他一些方式，下面將介紹兩個工具：原始碼檢查和效能分析。
16.3.1 使用 PyChecker 和 PyLint 檢查原始碼
長期以來，PyChecker（pychecker.sf.net）都是用於檢查Python原始碼的唯一工具，能夠找出 諸如給函式提供的引數不對等錯誤。（當然，標準庫中還有tabnanny，但沒那麼強大，只檢查縮 進是否正確。）之後出現了PyLint（pylint.org），它支援PyChecker提供的大部分功能，還有很多其 他的功能，如變數名是否符合指定的命名約定、你是否遵守了自己的編碼標準等。
安裝這些工具後，可以命令列指令碼的方式執行它們（PyChecker和PyLint對應的指令碼分別為 pychecker和pylint），也可將其作為Python模組（名稱與前面相同）。在Windows中，從命令列執行這兩個工具時，將分別使用批處理檔案pychecker.bat和 pylint.bat。因此，你可能需要將這兩個檔案加入環境變數PATH中，這樣才能從命令列執行 命令pychecker和pylint。
要使用PyChecker來檢查檔案，可執行這個指令碼並將檔名作為引數，如下所示：
pychecker file1.py file2.py ...
使用PyLint檢查檔案時，需要將模組（或包）名作為引數：
pylint module
PyChecker和PyLint都可作為模組（分別是pychecker.checker和pylint.lint）匯入，但它們 並不是為了以程式設計方式使用而設計的。匯入pychecker.checker時，它會檢查後續程式碼（包括匯入 的模組），並將警告列印到標準輸出。模組pylint.lint包含一個文件中沒有介紹的函式Run，這個 函式是供指令碼pylint本身使用的。
pychecker目前不支援python3.x。
pylint的使用很簡單，pylint目錄下（或設定系統路徑PATH）執行：
C:\Users\xx\AppData\Roaming\Python\Python37\Scripts>pylint manage.py
************* Module manage
manage.py:1:0: C0111: Missing module docstring (missing-docstring)
-----------------------------------
Your code has been rated at 8.89/10
C:\Users\zhx\AppData\Roaming\Python\Python37\Scripts>
就會看到其執行結果。
注：預設情況下pycharm預裝了pylint可以設定在選單tool下執行。進入PyCharm，從選單欄，依次進入： File -> Settings -> Tools -> External Tools。“+”，進行新增。需要填寫的部分分別是：“Name”，“Tool Settings -> Programs”、“Tool Settings -> Parameters”、“Tool Settings -> Working directory”。“Programs” 為pylint所在的目錄；Parameters 為$FilePath$即可在選單 Tools -> External Tools 中呼叫。
16.3.2 效能分析
標準庫包含一個卓越的效能分析模組profile，還有一個速度更快C語言版本，名為cProfile。 這個效能分析模組使用起來很簡單，只需呼叫其方法run並提供一個字串引數。
可以在cmd直接執行：
D:\>python -m cProfile test.py
3 function calls in 0.000 seconds
Ordered by: standard name
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.000 0.000 0.000 0.000 test.py:1(<module>)
1 0.000 0.000 0.000 0.000 {built-in method builtins.exec}
1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Prof
iler' objects}
D:\>
其中：“3 function calls in 0.000 seconds”是說呼叫了3個函式，用時0.00秒，其下為呼叫函式執行時間明細列表。
也可以引進入模組，形如：
#ptest.py
def foo():
sum = 0
for i in range(10000):
sum += i
sumA = bar()
sumB = bar()
return sum
def bar():
sum = 0
for i in range(100000):
sum += i
return sum
if __name__ == "__main__":
import cProfile
（待續）