An Empirical Study of Code Simplification Methods in Code Intelligence Tasks

doi:10.1145/3720540

An Empirical Study of Code Simplification Methods in Code Intelligence Tasks

2025-10-03

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DOI：10.1145/3720540

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In recent years, pre-trained language models have seen significant success in natural language processing and have been increasingly applied to code-related tasks. Code intelligence tasks have shown promising performance with the support of code pre-trained language models. Pre-processing code simplification methods have been introduced to prune code tokens from the model’s input while maintaining task effectiveness. These methods improve the efficiency of code intelligence tasks while reducing computational costs. Post-prediction code simplification methods provide explanations for code intelligence task outcomes, enhancing the reliability and interpretability of model predictions. However, comprehensive evaluations of these methods across diverse code pre-trained model architectures and code intelligence tasks are lacking. To assess the effectiveness of code simplification methods, we conduct an empirical study integrating these code simplification methods with various pre-trained code models across multiple code intelligence tasks.

An Empirical Study of Code Simplification Methods in Code Intelligence Tasks

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