Context: Code readability plays a critical role in software maintenance and evolvement, where a metric for classifying code readability levels is both applicable and desired.However, most prior research has treated code readability classification as a binary classification task due to the lack of labeled data.Objective: To support the training of multi-class code readability classification models, we propose an enhanced data augmentation approach.Method: The approach includes the use of domainspecific data transformation and GAN-based data augmentation.By virtue of this augmentation approach, we could generate sufficient readability data and well train a multi-class code readability model.Result: A series of experiments are conducted to evaluate our augmentation approach.The experimental results show that a state-of-the-art multi-class code readability classification accuracy of 68.0% is reached with a significant improvement of 6.3% compared to only using the original data.Conclusion: As an innovative work of proposing multi-class code readability classification and an enhanced code readability data augmentation approach, our method is proved to be effective.

Being a critical factor affecting the maintainability and reusability of the software, code readability is growing crucial in modern software development, where a metric for classifying code readability levels is both applicable and desired. However, most prior research has treated code readability classification as a binary classification task due to the lack of labeled data. To support the training of multi-class code readability classification models, we propose an enhanced data augmentation approach that could be used to generate sufficient readability data and well train a multi-class code readability model. The approach includes the use of domain-specific data transformation and GAN-based data augmentation. We conduct a series of experiments to verify our augmentation approach and gain a state-of-the-art multi-class code readability classification performance with 69.5% Micro-F1, 54.0% Macro-F1 and 67.7% Macro-AUC. Compared to the results where no augmented data is used, the improvements on Micro-F1, Macro-F1 and Macro-AUC are significant with 6.9%, 11.3% and 11.2%, respectively. As an innovative work of proposing multi-class code readability classification and an enhanced code readability data augmentation approach, our method is proved to be effective.