Why We Test and What to Test

Layer 5 begins. Every session so far has been manually verified by reading printed output. Automated tests replace that manual check with something repeatable and reliable.

1. Learning Objective

2. Pre-Coding Quiz

Answer these before reading the concept explanation or writing any code. It is fine to get these wrong — that is the point. You need 4/5 to proceed to the lab.

3. The Concept — Why We Test

Manual verification does not scale

Every lab so far ended the same way: run the file, read the printed output, and eyeball whether it looks right. This works for a single session, but it does not scale — nothing stops a later change from silently breaking Session 14's summary() while you are focused on Session 28's repository.

What an automated test actually gives you

A test is code that runs other code and checks the result automatically, every time, without a human needing to remember to look. If a change anywhere breaks an existing behavior, the test fails loudly and immediately — instead of the bug silently shipping unnoticed.

# Conceptually — this is what a test does, without any special tooling yet
def test_summary_format():
    k = Country(name="Kenya", region="Africa", population=54000000)
    result = k.summary()
    assert result == "Kenya (Africa): pop. 54,000,000"

test_summary_format()  # runs silently if it passes; raises AssertionError if not

Behavior vs implementation

A good test checks WHAT code does (its observable result), not HOW it does it internally. Testing implementation details makes tests fragile — they break during harmless refactors that did not actually change any real behavior.

# Good — tests behavior (the observable result)
def test_grow_population_increases_value():
    k = Country(name="Kenya", region="Africa", population=54000000)
    k.grow_population(1000000)
    assert k.population == 55000000

# Bad — tests an implementation detail that could change for unrelated reasons
# def test_grow_population_uses_plus_equals_operator():
#     ... inspecting the actual bytecode or source of grow_population ...

What to prioritize testing in this project

High value: Country's validated methods (set_population, grow_population), CountryRepository's data-fetching and validation logic, and CountryExplorer's computed properties. Lower value: one-line trivial getters with no logic, and anything that is purely cosmetic printing.

4. Lab

What you will build

A file called manual_tests.py — using bare assert statements, the same tool pytest builds on.

Step-by-step instructions

# manual_tests.py
class Country:
    def __init__(self, name, region, population):
        self.name = name
        self.region = region
        self.population = population

    def summary(self):
        return f"{self.name} ({self.region}): pop. {self.population:,}"

    def set_population(self, value):
        if value < 0:
            raise ValueError(f"population must be non-negative, got {value}")
        self.population = value

    def grow_population(self, amount):
        if amount < 0:
            raise ValueError(f"amount must be non-negative, got {amount}")
        self.population += amount

def test_summary_format():
    k = Country(name="Kenya", region="Africa", population=54000000)
    assert k.summary() == "Kenya (Africa): pop. 54,000,000"

test_summary_format()
print("test_summary_format passed")

def test_grow_population_increases_value():
    k = Country(name="Kenya", region="Africa", population=54000000)
    k.grow_population(1000000)
    assert k.population == 55000000

def test_grow_population_rejects_negative():
    k = Country(name="Kenya", region="Africa", population=54000000)
    try:
        k.grow_population(-5)
        assert False, "expected a ValueError but none was raised"
    except ValueError:
        pass  # expected

test_grow_population_increases_value()
test_grow_population_rejects_negative()
print("grow_population tests passed")

def test_set_population_rejects_and_preserves_state():
    k = Country(name="Kenya", region="Africa", population=54000000)
    try:
        k.set_population(-1)
        assert False, "expected a ValueError but none was raised"
    except ValueError:
        pass
    assert k.population == 54000000, "population should be unchanged after a rejected update"

test_set_population_rejects_and_preserves_state()
print("set_population rejection test passed")

5. Expected Files Changed

6. Commit Checkpoint

Once the lab is complete and you can explain every line, make this exact commit:

git add manual_tests.py docs/sessions/session-31/index.html
git commit -m "session-31: write manual assert-based tests for the highest-value logic first"

7. Code Review Checklist

Go through your code line by line and check each item:

8. Post-Coding Quiz

Same 5 questions. Take it again now that you have written and run the code. You need 4/5 to mark this session complete.

9. Reflection Questions

Think through these after the post-quiz. No right answer — they are for discussion.

10. What Breaks If This Knowledge Is Missing?

11. What We Learned