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

TL;DR

This paper systematically reviews human-centric studies on code readability and legibility, analyzing evaluation methods, variables, and competencies involved, to guide future research in understanding and measuring code comprehension.

Contribution

It provides a comprehensive analysis of 54 studies on code readability and legibility, categorizing evaluation approaches and modeling comprehension as a learning activity.

Findings

Most studies measure correctness or ask for opinions.

Few studies monitor physical signs like brain activation.

Different evaluation variables require different competencies.

Abstract

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.