Hypothesis

Hypothesis ist eine Bibliothek, mit der ihr Tests schreiben könnt, die aus einer Quelle von Beispielen parametrisiert werden. Anschließend werden einfache und verständliche Beispiele generiert, die dazu verwendet werden können, eure Tests fehlschlagen zu lassen und Fehler mit wenig Aufwand zu finden.

1. Installiert Hypothesis:

   Linux/macOS

   $ bin/python -m pip install hypothesis
   

   Windows

   C:> Scripts\python -m pip install hypothesis
   

   Alternativ kann Hypothesis auch mit Erweiterungen installiert werden, z.B.:

   Linux/macOS

   $ bin/python -m pip install hypothesis[numpy,pandas]
   

   Windows

   C:> Scripts\python -m pip install hypothesis[numpy,pandas]
   

2. Schreibt einen Test:

   1. Importe:

      import pytest
      from hypothesis import given
      from hypothesis.strategies import floats, lists
      

   2. Testen:

      @given(lists(floats(allow_nan=False, allow_infinity=False), min_size=1))
      def test_mean(ls):
          mean = sum(ls) / len(ls)
          assert min(ls) <= mean <= max(ls)
      

3. Test durchführen:

   Linux/macOS

   $ bin/python -m pytest test_hypothesis.py
   ============================= test session starts ==============================
   platform darwin -- Python 3.9.7, pytest-6.2.5, py-1.10.0, pluggy-1.0.0
   rootdir: /Users/veit/cusy/trn/python-basics/docs/test
   plugins: hypothesis-6.23.2
   collected 1 item
   
   test_hypothesis.py F                                                     [100%]
   
   =================================== FAILURES ===================================
   __________________________________ test_mean ___________________________________
   
       @given(lists(floats(allow_nan=False, allow_infinity=False), min_size=1))
   >   def test_mean(ls):
   
   test_hypothesis.py:6:
   _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
   
   ls = [9.9792015476736e+291, 1.7976931348623157e+308]
   
       @given(lists(floats(allow_nan=False, allow_infinity=False), min_size=1))
       def test_mean(ls):
           mean = sum(ls) / len(ls)
   >       assert min(ls) <= mean <= max(ls)
   E       assert inf <= 1.7976931348623157e+308
   E        +  where 1.7976931348623157e+308 = max([9.9792015476736e+291, 1.7976931348623157e+308])
   
   test_hypothesis.py:8: AssertionError
   ---------------------------------- Hypothesis ----------------------------------
   Falsifying example: test_mean(
       ls=[9.9792015476736e+291, 1.7976931348623157e+308],
   )
   =========================== short test summary info ============================
   FAILED test_hypothesis.py::test_mean - assert inf <= 1.7976931348623157e+308
   ============================== 1 failed in 0.44s ===============================
   

   Windows

   C:> Scripts\python -m pytest test_hypothesis.py
   ============================= test session starts ==============================
   platform win32 -- Python 3.9.7, pytest-6.2.5, py-1.10.0, pluggy-1.0.0
   rootdir: C:\Users\veit\python-basics\docs\test
   plugins: hypothesis-6.23.2
   collected 1 item
   
   test_hypothesis.py F                                                     [100%]
   
   =================================== FAILURES ===================================
   __________________________________ test_mean ___________________________________
   
       @given(lists(floats(allow_nan=False, allow_infinity=False), min_size=1))
   >   def test_mean(ls):
   
   test_hypothesis.py:6:
   _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
   
   ls = [9.9792015476736e+291, 1.7976931348623157e+308]
   
       @given(lists(floats(allow_nan=False, allow_infinity=False), min_size=1))
       def test_mean(ls):
           mean = sum(ls) / len(ls)
   >       assert min(ls) <= mean <= max(ls)
   E       assert inf <= 1.7976931348623157e+308
   E        +  where 1.7976931348623157e+308 = max([9.9792015476736e+291, 1.7976931348623157e+308])
   
   test_hypothesis.py:8: AssertionError
   ---------------------------------- Hypothesis ----------------------------------
   Falsifying example: test_mean(
       ls=[9.9792015476736e+291, 1.7976931348623157e+308],
   )
   =========================== short test summary info ============================
   FAILED test_hypothesis.py::test_mean - assert inf <= 1.7976931348623157e+308
   ============================== 1 failed in 0.44s ===============================
   

Siehe auch

Hypothesis for the Scientific Stack