# Evaluating Code Readability and Legibility: An Examination of Human-centric Studies

arXiv: https://arxiv.org/abs/2110.00785 · HTML: https://mlsanity.com/paper/2110.00785/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2021.

## Built on by (most cited first, 8 of 8)

- [Prover-Verifier Games improve legibility of LLM outputs](https://mlsanity.com/paper/2407.13692/) (2024)
- [Beyond Correctness: Benchmarking Multi-dimensional Code Generation for Large Language Models](https://mlsanity.com/paper/2407.11470/) (2024)
- [Assessing the Interpretability of Programmatic Policies with Large Language Models](https://mlsanity.com/paper/2311.06979/) (2023)
- [Basic Legibility Protocols Improve Trusted Monitoring](https://mlsanity.com/paper/2602.10153/) (2026)
- [See What I Mean? CUE: A Cognitive Model of Understanding Explanations](https://mlsanity.com/paper/2506.14775/) (2025)
- [Predicting the Understandability of Computational Notebooks through Code Metrics Analysis](https://mlsanity.com/paper/2406.10989/) (2024)
- [Towards Open Standards for Systemic Complexity in Digital Forensics](https://mlsanity.com/paper/2512.12970/) (2025)
- [BUILD-AND-FIND: An Effort-Aware Protocol for Evaluating Agent-Managed Codebases](https://mlsanity.com/paper/2605.06136/) (2026)

## Abstract (arXiv)

Reading code is an essential activity in software maintenance and evolution. Several studies with human subjects have investigated how different factors, such as the employed programming constructs and naming conventions, can impact code readability, i.e., what makes a program easier or harder to read and apprehend by developers, and code legibility, i.e., what influences the ease of identifying elements of a program. These studies evaluate readability and legibility by means of different comprehension tasks and response variables. In this paper, we examine these tasks and variables in studies that compare programming constructs, coding idioms, naming conventions, and formatting guidelines, e.g., recursive vs. iterative code. To that end, we have conducted a systematic literature review where we found 54 relevant papers. Most of these studies evaluate code readability and legibility by measuring the correctness of the subjects' results (83.3%) or simply asking their opinions (55.6%). Some studies (16.7%) rely exclusively on the latter variable.There are still few studies that monitor subjects' physical signs, such as brain activation regions (5%). Moreover, our study shows that some variables are multi-faceted. For instance, correctness can be measured as the ability to predict the output of a program, answer questions about its behavior, or recall parts of it. These results make it clear that different evaluation approaches require different competencies from subjects, e.g., tracing the program vs. summarizing its goal vs. memorizing its text. To assist researchers in the design of new studies and improve our comprehension of existing ones, we model program comprehension as a learning activity by adapting a preexisting learning taxonomy. This adaptation indicates that some competencies are often exercised in these evaluations whereas others are rarely targeted.