In Line with Context: Repository-Level Code Generation via Context Inlining

Repository-level code generation has attracted growing attention in recent years. Unlike function-level code generation, it requires the model to understand the entire repository and reason over complex dependencies across functions, classes, and modules. However, existing approaches such as retrieval-augmented generation (RAG) or context-based function selection often fall short; they primarily rely on surface-level similarity and struggle to capture the rich dependencies that govern repository-level semantics. In this paper, we introduce InlineCoder, a novel framework for repository-level code generation. InlineCoder enhances the understanding of repository context by inlining the unfinished function into its call graph, thereby reframing the challenge of repository understanding into a simpler function-level coding task. Given a function signature, InlineCoder first generates a draft completion (termed an "anchor"), which approximates downstream dependencies and enables perplexity-based confidence estimation. This anchor drives a bidirectional inlining process: (i) Upstream Inlining, which embeds the anchor into its callers to capture diverse usage scenarios; and (ii) Downstream Retrieval, which integrates the anchor's callees into the prompt to provide precise dependency context. The enriched context, combining draft completion with upstream and downstream perspectives, equips the LLM with a comprehensive repository view. Extensive experiments on the DevEval and RepoExec benchmarks demonstrate that InlineCoder substantially outperforms a wide range of state-of-the-art baselines, achieving average relative gains of 29.73% in EM, 20.82% in ES, and 49.34% in BLEU on RepoExec compared to the strongest baseline. These results highlight its effectiveness in repository context understanding as well as its generalization across domains.