Assessing Consensus of Developers' Views on Code Readability

TL;DR

This paper investigates the consensus among developers regarding code readability, highlighting areas of agreement and correlation with specific code aspects, to improve AI tools' alignment with developer perceptions.

Contribution

It provides empirical evidence of developer consensus on code readability and identifies key aspects influencing readability assessments.

Findings

Significant agreement among developers on readability evaluations

Certain code aspects strongly correlate with perceived readability

Insights for aligning AI models with developer perceptions

Abstract

The rapid rise of Large Language Models (LLMs) has changed software development, with tools like Copilot, JetBrains AI Assistant, and others boosting developers' productivity. However, developers now spend more time reviewing code than writing it, highlighting the importance of Code Readability for code comprehension. Our previous research found that existing Code Readability models were inaccurate in representing developers' notions and revealed a low consensus among developers, highlighting a need for further investigations in this field. Building on this, we surveyed 10 Java developers with similar coding experience to evaluate their consensus on Code Readability assessments and related aspects. We found significant agreement among developers on Code Readability evaluations and identified specific code aspects strongly correlated with Code Readability. Overall, our study sheds light on Code Readability within LLM contexts, offering insights into how these models can align with developers' perceptions of Code Readability, enhancing software development in the AI era.