Assessing Consensus of Developers’ Views on Code Readability

Agnia Sergeyuk, Olga Lvova, Sergey Titov, Anastasiia Serova, Farid Bagirov, and Timofey Bryksin

September, 2024. Published in the proceedings of PPIG'24 (Workshop).

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.