When 100% Test Coverage Isn’t Enough

📰 Medium · Python

100% test coverage doesn't guarantee code quality or correctness, and additional testing strategies are necessary to ensure reliability

intermediate Published 12 Apr 2026
Action Steps
  1. Write unit tests to cover different scenarios and edge cases
  2. Use integration testing to verify interactions between components
  3. Implement property-based testing to ensure code correctness
  4. Use fuzz testing to identify unexpected input handling
  5. Review and refactor code to improve maintainability and readability
Who Needs to Know This

Developers and QA engineers can benefit from understanding the limitations of test coverage and implementing additional testing strategies to ensure code reliability

Key Insight

💡 Test coverage is not a metric for code quality or correctness, and additional testing strategies are necessary to ensure reliability

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100% test coverage isn't enough! Use additional testing strategies like integration testing, property-based testing, and fuzz testing to ensure code reliability #testing #softwareengineering

Key Takeaways

100% test coverage doesn't guarantee code quality or correctness, and additional testing strategies are necessary to ensure reliability

Full Article

Title: When 100% Test Coverage Isn’t Enough

URL Source: https://medium.com/@oral.ersoy.dokumaci/when-100-test-coverage-isnt-enough-9faed3a2875c?source=rss------python-5

Published Time: 2026-04-12T19:50:25Z

Markdown Content:
# When 100% Test Coverage Isn’t Enough | by Oral Ersoy Dokumaci | Apr, 2026 | Medium

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# When 100% Test Coverage Isn’t Enough

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## Introduction

Suppose you are working in NLP or AI like half of the world including myself. You want to do some text cleaning to prepare training data for a multi-language model. Your goal is to remove text with duplicate meaning within each language. So you “write” a quick cleaning function, even dusting off the ancient language of regex that was once known to humans but only spoken by LLMs these days.

import re

def clean_text(text: str) -> str:

"""Normalize text for dedup."""

text = text.strip()

text = text.lower()

text = re.sub(r"\s+", " ", text) # collapse whitespace

text = re.sub(r"[^\w\s]", " ", text) # replace punctuation

return text
You run the function on an example input.

$ python -c "from preprocess import clean_text; print(clean_text('
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