class Doubler:
def __init__(self, n):
self._n = 2 * n
def n(self):
return self._n
x = Doubler(5)
print(x.n() == 10)
assert(x.n() == 10)
y = Doubler(-4)
print(y.n() == -8)
assert(y.n() == -8)True
TrueSeptember 15, 2025
Doubler?class Doubler:
def __init__(self, n):
self._n = 2 * n
def n(self):
return self._n
x = Doubler(5)
print(x.n() == 10)
assert(x.n() == 10)
y = Doubler(-4)
print(y.n() == -8)
assert(y.n() == -8)True
TrueDoubler classprint or assert statements?print and assertassert statements that will pass and fail as expected, decide which you prefer and why! What situations warrant a print statement and which ones require an assert?print statements require manual checking of outputassert statements automatically verify correctnesspytest or unittestcoverage.pyunittest for DayOfTheWeekimport unittest
from dayoftheweek import DayOfTheWeek
class TestDayOfTheWeek(unittest.TestCase):
def test_init(self):
d = DayOfTheWeek('F')
self.assertEqual(d.name(), 'Friday')
d = DayOfTheWeek('Th')
self.assertEqual(d.name(), 'Thursday')
unittest.main(argv=['ignored'], verbosity=2, exit=False)<unittest.main.TestProgram at 0x7f83f88ddcd0>unittest.main differently for tests outside Quartotest_dayoftheweek.py in slides/weekfour/OK output in terminal confirms passing assertionsDayOfTheWeekclass DayOfTheWeek:
"""A class to represent a day of the week."""
def __init__(self, abbreviation):
"""Create a new DayOfTheWeek object."""
self.abbreviation = abbreviation
self.name_map = {
"M": "Monday",
"T": "Tuesday",
"W": "Wednesday",
"Th": "Thursday",
"F": "Friday",
"Sa": "Saturday",
"Su": "Sunday",
}
def name(self):
return self.name_map.get(self.abbreviation)SaL1 = [1, 2, 3, 4, 5]
L2 = [6, 7, 8, 9, 10]
avg1 = sum(L1)/len(L1)
avg2 = sum(L2)/len(L2)
print("avg(", L1, ") -->", avg1)
print("avg(", L2, ") -->", avg2)avg( [1, 2, 3, 4, 5] ) --> 3.0
avg( [6, 7, 8, 9, 10] ) --> 8.0L1 = [1, 2, 3, 4, 5]
L2 = [6, 7, 8, 9, 10]
if len(L1) == 0:
avg1 = 0
else:
avg1 = sum(L1) / len(L1)
if len(L2) == 0:
avg2 = 0
else:
avg2 = sum(L2) / len(L2)
print("avg(", L1, ") -->", avg1)
print("avg(", L2, ") -->", avg2)avg( [1, 2, 3, 4, 5] ) --> 3.0
avg( [6, 7, 8, 9, 10] ) --> 8.0def avg(L):
if len(L) == 0:
return 0
else:
return sum(L) / len(L)
L1 = [1, 2, 3, 4, 5]
L2 = [6, 7, 8, 9, 10]
avg1 = avg(L1)
avg2 = avg(L2)
print("avg(", L1, ") -->", avg1)
print("avg(", L2, ") -->", avg2)avg( [1, 2, 3, 4, 5] ) --> 3.0
avg( [6, 7, 8, 9, 10] ) --> 8.0avg function avoids the defect and is easier to read!L1 and L2, try to find the defect. Can you make a solution that works for empty lists? How do you know it is correct?coverage.pySoftware testing helps refine document engineering tool design
Interplay between testing and document tool design:
import re
from typing import Dict, Any
def document_summary(text: str) -> Dict[str, Any]:
"""Generate a comprehensive summary of document statistics."""
words = [word for word in text.split() if any(char.isalnum() for char in word)]
word_count = len(words)
sentences = re.split(r'[.!?]+', text)
sentence_count = len([s for s in sentences if s.strip()])
paragraphs = [p for p in text.split('\n\n') if p.strip()]
paragraph_count = len(paragraphs)
avg_words_per_sentence = word_count / sentence_count if sentence_count > 0 else 0
return {
'word_count': word_count, 'sentence_count': sentence_count,
'paragraph_count': paragraph_count,
'avg_words_per_sentence': round(avg_words_per_sentence, 1)
}
sample_text = "Hello world. This is a test."
result = document_summary(sample_text)
print(f"Words: {result['word_count']}, Sentences: {result['sentence_count']}")
assert result['word_count'] == 6
assert result['sentence_count'] == 2Words: 6, Sentences: 2document_summary functiondocument_summarysample_textresultassert statements to confirm correctness of outputpytest! Frameworks make testing easy and repeatable.uv to add pytest as a project dependency!pytest and unittest:
pytesthypothesisDayOfTheWeek with Pytestfrom daydetector.dayoftheweek import DayOfTheWeek
def test_init():
"""Test the DayOfTheWeek class."""
d = DayOfTheWeek("F")
assert d.name() == "Friday"
d = DayOfTheWeek("Th")
assert d.name() == "Thursday"
d = DayOfTheWeek("W")
assert d.name() == "Wednesday"
d = DayOfTheWeek("T")
assert d.name() == "Tuesday"
d = DayOfTheWeek("M")
assert d.name() == "Monday"pytest@pytest.mark.parametrize(
"abbreviation, expected",
[
("M", "Monday"),
("T", "Tuesday"),
("W", "Wednesday"),
("Th", "Thursday"),
("F", "Friday"),
("Sa", "Saturday"),
("Su", "Sunday"),
("X", None),
],
)
def test_day_name(abbreviation, expected):
"""Use parameterized testing to confirm that lookup works correctly."""
day = DayOfTheWeek(abbreviation)
assert day.name() == expectedimport hypothesis.strategies as st
from hypothesis import given
import pytest
@pytest.mark.parametrize(
"valid_days",
[["Monday", "Tuesday", "Wednesday", "Thursday",
"Friday", "Saturday", "Sunday"]],
)
@given(
st.text(alphabet=st.characters(), min_size=1, max_size=2)
)
def test_abbreviation_maps_to_name(valid_days, abbreviation):
"""Use property-based testing with Hypothesis to confirm mapping."""
day = DayOfTheWeek(abbreviation)
assert day.name() in valid_days or day.name() is NoneHypothesis strategies generate random character inputs for the abbreviation parameter, thereby increasing the input diversitytest_dayoftheweek.py! Wow!What are the benefits and downsides of using artificial intelligence (AI) to generate tests?
What are situations in which you should and should not use AI to generate tests?
Tests establish a confidence in correctness!
ds2 package in the donsheehy/datastructures GitHub repositoryProsegrammers