Code Readability Assessment Method Based on Multidimensional Features and Hybrid Neural Networks

Abstract

Quantitative and accurate assessment of code readability is an important way to ensure software quality,reduce communication and maintenance costs,and improve the efficiency of software development and evolution.However,existing code readability studies depend mainly on the manual feature engineering method,which is likely to limit the model performance due to factors such as code representation strategies and technical means.Unlike prior studies,we propose a novel code readability assessment method based on multidimensional features and hybrid neural networks by using the technique of deep learning.Specifi-cally,we first propose a representation strategy with different granularity levels to transform source codes into matrices and vectors as the input to deep neural networks.We then build a CNN-BiGRU hybrid neural network that can automatically learn structural and semantic features from the source code.The experimental results show that our method is able to achieve an accuracy of 84.6%,which is 9.2% higher than CNN alone and 6.5% higher than BiGRU alone.Moreover,our method can outperform five state-of-the-art code readability models,which confirms the feasibility and effectiveness of multidimensional features and hybrid neural networks proposed in this study.

Keywords

• code readability|code representation|deep learning|code analysis|software quality assurance