Do Automatic Comment Generation Techniques Fall Short? Exploring the Influence of Method Dependencies on Code Understanding

Abstract

Method-level comments are critical for improving code comprehension and supporting software maintenance. With advancements in large language models (LLMs), automated comment generation has become a major research focus. However, existing approaches often overlook method dependencies, where one method relies on or calls others, affecting comment quality and code understandability. This study investigates the prevalence and impact of dependent methods in software projects and introduces a dependency-aware approach for method-level comment generation. Analyzing a dataset of 10 popular Java GitHub projects, we found that dependent methods account for 69.25% of all methods and exhibit higher engagement and change proneness compared to independent methods. Across 448K dependent and 199K independent methods, we observed that state-of-the-art fine-tuned models (e.g., CodeT5+, CodeBERT) struggle to generate comprehensive comments for dependent methods, a trend also reflected in LLM-based approaches like ASAP. To address this, we propose HelpCOM, a novel dependency-aware technique that incorporates helper method information to improve comment clarity, comprehensiveness, and relevance. Experiments show that HelpCOM outperforms baseline methods by 5.6% to 50.4% across syntactic (e.g., BLEU), semantic (e.g., SentenceBERT), and LLM-based evaluation metrics. A survey of 156 software practitioners further confirms that HelpCOM significantly improves the comprehensibility of code involving dependent methods, highlighting its potential to enhance documentation, maintainability, and developer productivity in large-scale systems.