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

• URL: http://arxiv.org/abs/2601.00376v1
• Date: Thu, 01 Jan 2026 15:56:24 GMT
• Title: In Line with Context: Repository-Level Code Generation via Context Inlining
• Authors: Chao Hu, Wenhao Zeng, Yuling Shi, Beijun Shen, Xiaodong Gu,
• Abstract summary: In this paper, we introduce InlineCoder, a novel framework for repository-level code generation.<n>InlineCoder enhances the understanding of repository context by inlining the unfinished function into its call graph.
• Score: 11.065371614078723
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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, reasoning 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 challenging repository understanding as an easier 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.

Related papers

• Do Not Treat Code as Natural Language: Implications for Repository-Level Code Generation and Beyond [13.550121154853715]
  We present Hydra, a repository-level code generation framework that treats code as structured code rather than natural language.<n>We show that Hydra achieves state-of-the-art performance across open- and closed-source CodeLLMs.
  arXiv  Detail & Related papers  (2026-02-12T07:44:00Z)
• AlignCoder: Aligning Retrieval with Target Intent for Repository-Level Code Completion [55.21541958868449]
  We propose AlignCoder, a repository-level code completion framework.<n>Our framework generates an enhanced query that bridges the semantic gap between the initial query and the target code.<n>We employ reinforcement learning to train an AlignRetriever that learns to leverage inference information in the enhanced query for more accurate retrieval.
  arXiv  Detail & Related papers  (2026-01-27T15:23:14Z)
• Completion by Comprehension: Guiding Code Generation with Multi-Granularity Understanding [37.78627994991325]
  CoCo is a novel framework that enables code Completion by of multi-granularity context from large-scale code repositories.<n>Experiments on CrossCodeEval and RepoEval benchmarks demonstrate that CoCo consistently surpasses state-of-the-art baselines.
  arXiv  Detail & Related papers  (2025-12-04T07:37:59Z)
• RepoScope: Leveraging Call Chain-Aware Multi-View Context for Repository-Level Code Generation [16.544483144957407]
  RepoScope is a call chain-aware multi-view context for repository-level code generation.<n>We present a novel call chain prediction method that utilizes the repository's structural semantics to improve the identification of callees in the target function.<n>RepoScope outperforms state-of-the-art methods, achieving up to a 36.35% relative improvement in pass@1 scores.
  arXiv  Detail & Related papers  (2025-07-20T02:35:36Z)
• EpiCoder: Encompassing Diversity and Complexity in Code Generation [66.43738008739555]
  Existing methods for code generation use code snippets as seed data.<n>We introduce a novel feature tree-based synthesis framework, which revolves around hierarchical code features.<n>Our framework provides precise control over the complexity of the generated code, enabling functionalities that range from function-level operations to multi-file scenarios.
  arXiv  Detail & Related papers  (2025-01-08T18:58:15Z)
• CodeRAG-Bench: Can Retrieval Augment Code Generation? [78.37076502395699]
  We conduct a systematic, large-scale analysis of code generation using retrieval-augmented generation.<n>We first curate a comprehensive evaluation benchmark, CodeRAG-Bench, encompassing three categories of code generation tasks.<n>We examine top-performing models on CodeRAG-Bench by providing contexts retrieved from one or multiple sources.
  arXiv  Detail & Related papers  (2024-06-20T16:59:52Z)
• On the Impacts of Contexts on Repository-Level Code Generation [5.641402231731082]
  We present RepoExec, a novel benchmark designed to evaluate repository-level code generation.<n>We focus on three key aspects: executability, functional correctness through comprehensive test case generation, and accurate utilization of cross-file contexts.
  arXiv  Detail & Related papers  (2024-06-17T10:45:22Z)
• GraphCoder: Enhancing Repository-Level Code Completion via Code Context Graph-based Retrieval and Language Model [30.625128161499195]
  GraphCoder is a retrieval-augmented code completion framework. It uses general code knowledge and the repository-specific knowledge via a graph-based retrieval-generation process. It achieves higher exact match (EM) on average, with increases of +6.06 in code match and +6.23 in identifier match, while using less time and space.
  arXiv  Detail & Related papers  (2024-06-11T06:55:32Z)
• On The Importance of Reasoning for Context Retrieval in Repository-Level Code Editing [82.96523584351314]
  We decouple the task of context retrieval from the other components of the repository-level code editing pipelines. We conclude that while the reasoning helps to improve the precision of the gathered context, it still lacks the ability to identify its sufficiency.
  arXiv  Detail & Related papers  (2024-06-06T19:44:17Z)
• Alibaba LingmaAgent: Improving Automated Issue Resolution via Comprehensive Repository Exploration [64.19431011897515]
  This paper presents Alibaba LingmaAgent, a novel Automated Software Engineering method designed to comprehensively understand and utilize whole software repositories for issue resolution.<n>Our approach introduces a top-down method to condense critical repository information into a knowledge graph, reducing complexity, and employs a Monte Carlo tree search based strategy.<n>In production deployment and evaluation at Alibaba Cloud, LingmaAgent automatically resolved 16.9% of in-house issues faced by development engineers, and solved 43.3% of problems after manual intervention.
  arXiv  Detail & Related papers  (2024-06-03T15:20:06Z)
• RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generation [96.75695811963242]
  RepoCoder is a framework to streamline the repository-level code completion process. It incorporates a similarity-based retriever and a pre-trained code language model. It consistently outperforms the vanilla retrieval-augmented code completion approach.
  arXiv  Detail & Related papers  (2023-03-22T13:54:46Z)
• A Transformer-based Approach for Source Code Summarization [86.08359401867577]
  We learn code representation for summarization by modeling the pairwise relationship between code tokens. We show that despite the approach is simple, it outperforms the state-of-the-art techniques by a significant margin.
  arXiv  Detail & Related papers  (2020-05-01T23:29:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.