REPOFUSE: Repository-Level Code Completion with Fused Dual Context
CoRR(2024)
摘要
The success of language models in code assistance has spurred the proposal of
repository-level code completion as a means to enhance prediction accuracy,
utilizing the context from the entire codebase. However, this amplified context
can inadvertently increase inference latency, potentially undermining the
developer experience and deterring tool adoption-a challenge we termed the
Context-Latency Conundrum. This paper introduces RepoGenix, a pioneering
solution designed to enhance repository-level code completion without the
latency trade-off. RepoGenix uniquely fuses two types of contexts: the analogy
context, rooted in code analogies, and the rationale context, which encompasses
in-depth semantic relationships. We propose a novel rank truncated generation
(RTG) technique that efficiently condenses these contexts into prompts with
restricted size. This enables RepoGenix to deliver precise code completions
while maintaining inference efficiency. Through testing with the CrossCodeEval
suite, RepoGenix has demonstrated a significant leap over existing models,
achieving a 40.90
completions and a 26.8
validation, RepoGenix has been integrated into the workflow of a large
enterprise, where it actively supports various coding tasks.
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