How to Analyze a README File Using Readability Metrics in Python

By Suman Saurabh - May 31, 2025

Introduction

A good README.md file is often the difference between a project that welcomes contributors and one that drives them away. Whether you're maintaining an open source library or evaluating internal documentation, it's helpful to measure the clarity of your README using well-known readability metrics.

In this blog, we'll walk through:

• Why readability matters in technical READMEs
• What metrics are useful
• How to calculate them using Python
• How to interpret the results
• How readability metrics can be combined with Large Language Models (LLMs) to further enhance documentation quality

Why Readability Metrics?

While code speaks for itself, your README must communicate effectively with humans—developers, stakeholders, and even recruiters. Research has consistently shown that readability significantly impacts user engagement and comprehension. For instance, a study by DuBay (2004) highlights how readability directly influences reader retention and understanding, emphasizing the importance of clear and accessible documentation.

When applied to README files, readability metrics help answer:

• Is the documentation beginner-friendly?
• Are sentences too long or jargon-heavy?
• Could the structure be simplified?

Key Readability Metrics

Here are the most commonly used readability scores, supported by extensive research:

• Flesch Reading Ease: Ranges from 0 (very hard) to 100 (very easy). Proven effective in assessing general readability (Flesch, 1948).
• Flesch-Kincaid Grade Level: Converts the ease score into a U.S. school grade level, widely used in educational contexts (Kincaid et al., 1975).
• Gunning Fog Index: Estimates the education level needed to understand the text, useful for technical documentation (Gunning, 1952).
• SMOG Index: Predicts the years of education needed based on polysyllable count, highly accurate for technical and health-related texts (McLaughlin, 1969).
• Dale-Chall Score: Compares words used in the text with a list of familiar words, effective for assessing beginner-friendliness (Dale & Chall, 1948).
• Automated Readability Index (ARI): Uses characters per word and words per sentence, suitable for automated readability assessments (Smith & Senter, 1967).

Python Code to Calculate Readability Metrics

We'll use the textstat library to calculate these metrics. First, install it:

pip install textstat

Step 1: Load the README file

import os

def read_readme_file(path="README.md"):
    if os.path.exists(path):
        with open(path, "r", encoding="utf-8") as file:
            return file.read()
    else:
        raise FileNotFoundError("README.md not found")

Step 2: Analyze Readability

import textstat

class TextStatistics:
    def __init__(self, content: str):
        self.content = content

    def get_metrics(self):
        return {
            "flesch_reading_ease": textstat.flesch_reading_ease(self.content),
            "flesch_kincaid_grade": textstat.flesch_kincaid_grade(self.content),
            "gunning_fog_index": textstat.gunning_fog(self.content),
            "smog_index": textstat.smog_index(self.content),
            "dale_chall": textstat.dale_chall_readability_score(self.content),
            "automated_readability_index": textstat.automated_readability_index(self.content),
            "avg_sentence_length": textstat.avg_sentence_length(self.content),
            "syllable_per_word": textstat.avg_syllables_per_word(self.content),
            "poly_syllable_count": textstat.polysyllabcount(self.content),
            "word_count": textstat.lexicon_count(self.content),
            "reading_time_sec": textstat.reading_time(self.content, ms_per_char=14.69),
            "line_count": len(self.content.strip().splitlines())
        }

Step 3: Print the Results

if __name__ == "__main__":
    content = read_readme_file("README.md")
    stats = TextStatistics(content)
    metrics = stats.get_metrics()

    for k, v in metrics.items():
        print(f"{k.replace('_', ' ').title()}: {v}")

How to Interpret the Results

Here's a general guide based on readability research:

• Flesch Reading Ease > 60: Good readability for general audiences.
• Flesch-Kincaid Grade < 9: Easy to follow for most readers.
• Fog Index < 12: Clear and concise, suitable for technical documentation.
• Dale-Chall < 8.0: Beginner-friendly and accessible.
• Average Sentence Length < 20 words: Optimal for comprehension.

If your README has very high scores (grade level > 12 or fog index > 15), consider simplifying the language, shortening sentences, or breaking down complex sections.

Integrating Readability Metrics with Large Language Models (LLMs)

Readability metrics provide quantitative insights into textual complexity, but they don't directly suggest improvements. Integrating these metrics with Large Language Models (LLMs) like GPT-4 can bridge this gap. LLMs can:

• Automatically simplify complex sentences identified by readability metrics.
• Suggest clearer wording or synonyms for jargon-heavy terms.
• Generate beginner-friendly explanations for technical concepts.
• Provide structural recommendations to enhance readability and engagement.

Recent research (Brown et al., 2020) demonstrates that LLMs effectively rewrite and simplify text, making them ideal companions to readability metrics for improving documentation quality.

Advanced Use Cases

Readability analysis can be extended beyond individual README files to address more complex scenarios. Here are some advanced use cases:

• Automating Readability Analysis for Repositories: By writing scripts to iterate through multiple files in a repository, you can automate the readability analysis process. This is particularly useful for large projects with extensive documentation.
• Integrating Readability Analysis into CI/CD Pipelines: Incorporating readability checks into CI/CD workflows ensures that documentation meets quality standards before being merged or deployed. This can be achieved by running the analysis as part of pre-commit hooks or build pipelines.

These advanced use cases help maintain consistent documentation quality across projects and teams, saving time and effort in manual reviews.

Comparison of Tools

When it comes to readability analysis, several tools and libraries are available, each with its own strengths and weaknesses. Below is a detailed comparison of some popular options:

1. Textstat (Python Library)

• Overview: textstat is a Python library designed for calculating a wide range of readability metrics. It is widely used for its simplicity and comprehensive support for multiple metrics.
• Features:
  • Supports metrics like Flesch Reading Ease, Gunning Fog Index, SMOG Index, and more.
  • Provides additional statistics like average sentence length, syllables per word, and word count.
  • Easy to integrate into Python scripts for automation.
• Pros:
  • Simple and intuitive API.
  • Actively maintained with regular updates.
  • Lightweight and fast.
• Cons:
  • Limited customization for specific use cases.
  • Focused solely on readability metrics without advanced text processing features.
• Best For: Developers looking for a quick and easy way to calculate readability metrics programmatically.

2. Readability-Score (Python Library)

• Overview: readability-score is another Python library that provides readability metrics similar to textstat. It includes additional features like text highlighting for complex sentences.
• Features:
  • Calculates common readability scores like Flesch Reading Ease and Gunning Fog Index.
  • Highlights difficult sentences in the text for easier identification.
  • Supports basic text preprocessing.
• Pros:
  • Includes sentence highlighting for better visualization.
  • Offers similar metrics to textstat.
• Cons:
  • Slightly more complex setup compared to textstat.
  • Less active development and community support.
• Best For: Users who need visual feedback on text complexity.

3. Microsoft Word Readability Statistics

• Overview: Microsoft Word includes built-in readability statistics as part of its spelling and grammar check feature. It calculates metrics like Flesch Reading Ease and Flesch-Kincaid Grade Level.
• Features:
  • Provides readability scores alongside grammar and spelling suggestions.
  • Integrated into the Microsoft Word interface.
• Pros:
  • No additional setup required for Word users.
  • Combines readability analysis with grammar and spelling checks.
• Cons:
  • Requires manual input of text into Word.
  • Not scriptable or automatable for large-scale analysis.
• Best For: Individual users analyzing small documents manually.

4. Hemingway Editor (Desktop and Online Tool)

• Overview: Hemingway Editor is a popular tool for improving the readability of text. It highlights complex sentences, passive voice, and adverbs.
• Features:
  • Provides a readability grade level.
  • Highlights areas of improvement in the text.
  • Suggests simpler alternatives for complex phrases.
• Pros:
  • Intuitive and user-friendly interface.
  • Focuses on actionable improvements.
• Cons:
  • Limited to manual input and analysis.
  • Does not provide detailed readability metrics like Flesch Reading Ease.
• Best For: Writers looking to simplify and improve their text manually.

5. Online Readability Tools

• Overview: Several online tools, such as Readable.com and Online-Utility.org, offer readability analysis without requiring installation.
• Features:
  • Calculate multiple readability scores.
  • Provide additional insights like keyword density and text structure.
• Pros:
  • Easy to use with no installation required.
  • Accessible from any device with an internet connection.
• Cons:
  • Limited to individual files or small text inputs.
  • Lack of integration capabilities for automation.
• Best For: Quick, one-off readability checks.

6. Natural Language Toolkit (NLTK) and SpaCy (Python Libraries)

• Overview: While not specifically designed for readability analysis, NLTK and SpaCy are powerful natural language processing (NLP) libraries that can be used to calculate custom readability metrics.
• Features:
  • Tokenization, part-of-speech tagging, and syntactic parsing.
  • Customizable for advanced text analysis.
• Pros:
  • Highly flexible and extensible.
  • Suitable for advanced NLP tasks beyond readability.
• Cons:
  • Requires more effort to implement readability metrics.
  • Steeper learning curve compared to dedicated readability tools.
• Best For: Advanced users who need full control over text analysis.

Summary Table

Tool/Library	Metrics Supported	Automation	Visualization	Ease of Use	Best For
Textstat	Comprehensive	Yes	No	High	Developers needing quick metrics
Readability-Score	Comprehensive + Sentence Highlight	Yes	Yes	Medium	Visual feedback on complexity
MS Word	Flesch, Flesch-Kincaid	No	No	High	Manual analysis in Word
Hemingway Editor	Readability Grade + Suggestions	No	Yes	High	Simplifying text manually
Online Tools	Varies	No	No	High	Quick, one-off checks
NLTK/SpaCy	Customizable	Yes	No	Low	Advanced NLP tasks

This detailed comparison should help you choose the right tool based on your specific needs, whether it's automation, visualization, or advanced customization.

Conclusion

Readability metrics offer an objective way to evaluate your README.md file. While they don't capture technical correctness or code clarity, they highlight structural and linguistic complexity, guiding you toward clearer, more accessible documentation.

Combining readability metrics with LLM-based tools can significantly enhance your README, making it more engaging and understandable for diverse audiences. This powerful combination ensures your documentation not only informs but also welcomes and retains contributors.

This is exactly what we're solving at Penify. Penify leverages readability metrics and advanced LLMs to help you create exceptional documentation effortlessly. Try it out today at www.Penify.dev!

References

• Brown, T. B., Mann, B., Ryder, N., et al. (2020). Language Models are Few-Shot Learners. arXiv preprint arXiv:2005.14165. Link
• Dale, E., & Chall, J. S. (1948). A formula for predicting readability. Educational Research Bulletin, 27(1), 11-28.Link
• DuBay, W. H. (2004). The Principles of Readability. Impact Information.Link
• Flesch, R. (1948). A new readability yardstick. Journal of Applied Psychology, 32(3), 221-233. Link
• Flesch-Kincaid Readability Tests. (n.d.). Readable. Link
• Gunning, R. (1952). The Technique of Clear Writing. McGraw-Hill.Link
• Kincaid, J. P., Fishburne, R. P., Rogers, R. L., & Chissom, B. S. (1975). Derivation of new readability formulas for Navy enlisted personnel. Research Branch Report 8-75.Link
• McLaughlin, G. H. (1969). SMOG grading—a new readability formula. Journal of Reading, 12(8), 639-646. Link
• Smith, E. A., & Senter, R. J. (1967). Automated readability index. AMRL-TR-66-220.Link