Quote from QABash.ai on 14 April 2025, 2:56 PM

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🚀 Ever Had a Test Fail… and Stop Everything?

Imagine this: You're running a huge automated test suite. One test fails—and boom! The whole execution stops. You’re left wondering what happened to the rest of the test cases. Annoying, right?

That’s where the concept of Hard vs. Soft Assertions in Pytest comes into play. Whether you're building automation scripts or doing exploratory validation, knowing when to stop a test and when to keep going can make a huge difference in your debugging game.

Let’s break it down in a way your inner 5-year-old automation engineer will totally get. 👇

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What’s the Difference?

🧱 Hard Assertions

Think of a hard assert like a brick wall. The moment you hit it, the journey stops.

assert actual == expected

If this fails, the test stops right there. Any remaining steps? Ignored. 💥

🧊 Soft Assertions

Soft asserts are more like gentle reminders. If something’s wrong, it notes the issue and keeps going.

In Pytest, soft assertions aren’t built-in — but with the help of plugins like pytest-check, you can collect multiple assertion results in one test case.

import pytest_check as check

check.equal(actual, expected)
check.is_true(condition)

The test continues even after a failed check, and all failures are reported at the end. 🎯

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🔍 Why This Matters for SDETs

As an SDET, you’re not just running tests—you’re helping teams ship faster with confidence. Soft assertions let you:

• Capture multiple failures in one run
• Avoid the noise of re-running tests just to catch the next failure
• Improve test coverage diagnostics

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🛠️ Real-World Example

✅ Hard Assert Example

def test_hard_assert_example():
    assert 1 == 2  # Fails here, test stops
    assert "hello" == "hello"

Only the first assert is executed. Second one? Never reached. 😬

✅ Soft Assert Example with pytest-check

import pytest_check as check

def test_soft_assert_example():
    check.equal(1, 2)  # Logged as failure
    check.equal("hello", "hello")  # Still runs

Both lines are executed. Failures collected and shown together. 🧾

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💡 Pro Tips to Assert Like a Pro

✅ Use Soft Asserts for UI/API Field Validations
Perfect for checking multiple fields or endpoints in one go 🧪

✅ Stick to Hard Asserts for Setup Validations
If a precondition fails, no point continuing! ❌

✅ Use pytest-check or softest plugin for Soft Asserts
Easy to plug into your framework 🔌

✅ Combine Both Strategically
Start your test with hard assertions for critical checks, then validate the rest softly 📊

✅ Log Failures with Context
Always log what and why when things fail 📝

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⚠️ Common Mistakes to Avoid

🚫 Using only hard asserts in complex test cases – You’ll miss half the picture.

🚫 Not installing plugins for soft assertions – Pytest doesn’t do it natively!

🚫 Not reporting soft assert failures clearly – They’re easy to miss if not logged properly.

🚫 Running soft asserts in setup/fixtures – That can cause flaky or misleading results.

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🎓 Expert Insight

"Soft assertions are like health checkups — they help you catch multiple issues in one go. But don’t skip hard asserts where it really matters."
— A Wise SDET Mentor 😉

Also, tools like Allure Reports or pytest-html make it easier to visually inspect soft vs hard failures in your CI/CD pipeline.

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Final Thoughts — Know When to Be Hard, and When to Go Soft 😉

Mastering assertions is one of those underrated skills that separates the average tester from the automation Jedi. Start using soft assertions when you're validating data. Stick to hard assertions when setting up environments or critical flows.