IntentContextCS: Intent-specific Context Selection for Multi-Intent Code Comment Generation

Code comment generation aims to produce concise natural-language comments that help developers understand and maintain source code. Recent work has expanded the input to include information outside the target method, but most approaches still treat context as a single pool of evidence. This is limiting in multi-intent summarization, because a comment that explains what a method does may require different evidence from a comment that explains how to use the method or why it exists. We propose IntentContextCS, a framework for multi-intent code comment generation that selects context according to the intended documentation goal. The framework combines three selection signals: token overlap between the target method and context types, semantic similarity based on CodeBERT embeddings, and reasoning-based context ranking using large language models. These signals are used to construct an intent-specific policy over five context types: callers, callees, sibling methods, class attributes, and project-level information. On the CodeSearchNet Java dataset, the empirical analysis shows that intent categories have different context profiles. For example, caller contexts are most useful for usage-oriented comments, whereas callee and sibling contexts are more informative for comments about functionality and implementation. In the generation experiments, IntentContextCS improves METEOR by 8.9%, USE by 3.5%, and SIDE by 3.0% over the strongest full-context LLM baseline, while using 54% fewer context tokens. These results suggest that, for code comment generation, selecting the right context is more effective than simply providing more context.