Code generation under long contexts is becoming increasingly critical as
Large Language Models (LLMs) are required to reason over extensive information
in the codebase. While recent advances enable code LLMs to process long inputs,
high API costs and generation latency remain substantial bottlenecks. Existing
context pruning techniques, such as LLMLingua, achieve promising results for
general text but overlook code-specific structures and dependencies, leading to
suboptimal performance in programming tasks. In this paper, we propose
LongCodeZip, a novel plug-and-play code compression framework designed
specifically for code LLMs. LongCodeZip employs a dual-stage strategy: (1)
coarse-grained compression, which identifies and ranks function-level chunks
using conditional perplexity with respect to the instruction, retaining only
the most relevant functions; and (2) fine-grained compression, which segments
retained functions into blocks based on perplexity and selects an optimal
subset under an adaptive token budget to maximize relevance. Evaluations across
multiple tasks, including code completion, summarization, and question
answering, show that LongCodeZip consistently outperforms baseline methods,
achieving up to a 5.6x compression ratio without degrading task performance. By
effectively reducing context size while preserving essential information,
LongCodeZip enables LLMs to better scale to real-world, large-scale code
scenarios, advancing the efficiency and capability of code intelligence
applications.