# An Empirical Study of the Relationships between Code Readability and Software Complexity

arXiv: https://arxiv.org/abs/1909.01760 · HTML: https://mlsanity.com/paper/1909.01760/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2019.

## Built on by (most cited first, 3 of 3)

- [What's Wrong with Your Code Generated by Large Language Models? An Extensive Study](https://mlsanity.com/paper/2407.06153/) (2024)
- [Code Readability in the Age of Large Language Models: An Industrial Case Study from Atlassian](https://mlsanity.com/paper/2501.11264/) (2025)
- [Characterizing Readability Issue Patterns and the Role of Prompt Design in LLM-Generated Code](https://mlsanity.com/paper/2605.13280/) (2026)

## Abstract (arXiv)

Code readability and software complexity are important software quality metrics that impact other software metrics such as maintainability, reusability, portability and reliability. This paper presents an empirical study of the relationships between code readability and program complexity. The results are derived from an analysis of 35 Java programs that cover 23 distinct code constructs. The analysis includes six readability metrics and two complexity metrics. Our study empirically confirms the existing wisdom that readability and complexity are negatively correlated. Applying a machine learning technique, we also identify and rank those code constructs that substantially affect code readability.