Repository-Level Context Files

• Repository-level context files are structured data artifacts that systematically encode cross-file dependencies such as import structures, call graphs, and symbol definitions.
• They are created using methods like heuristic extraction, retrieval-augmented generation, and graph-based pruning to optimally capture repository-wide insights.
• These files enhance code generation, editing, and repair while posing challenges in balancing context size with LLM input limitations.

A repository-level context file is an artifact or data structure—often a text or serialized file—constructed to supply LLMs or autonomous coding agents with salient, non-local information extracted from an entire code repository. Such files systematically encode information like import structures, type hierarchies, call graphs, symbol definitions, or task-specific cross-file dependencies, thereby enabling code models or agents to execute repository-level code generation, completion, repair, or editing tasks that require broader context than the current buffer. The design and utility of repository-level context files have been the subject of recent, rigorous exploration across prompt engineering, retrieval-augmented generation, static and dynamic analysis, and tool-mediated agent systems.

1. Formal Definitions and Taxonomy of Repository-Level Context

A repository-level context file aims to aggregate context beyond the local snippet or file, addressing the challenge that LLMs’ input windows are insufficient to directly “see” all relevant code in large projects. The definition and modeling of repository context span several concrete instantiations:

• Prompt Proposal–Based Contexts: A code repository $R$ of $n$ files is modeled as $C = \{C_1, C_2, ..., C_n\}$, the set of file-level contexts. A prompt proposal $p$ is a function that, for a target (e.g., a completion hole $h$), selects a subset of files $S_p \subseteq R$ and extracts a structured context $f_p(h, C) \in \mathcal{S}$ (where $\mathcal{S}$ is a space of prompt strings) (Shrivastava et al., 2022).
• Knowledge Graph–Augmented Contexts: Nodes represent repository artifacts (issues, PRs, files, classes, functions), and edges encode relationships (containment, calls, textual references, etc.) $G = (V, E, w)$ (Yang et al., 27 Mar 2025). Shortest-path extraction through the knowledge graph surfaces contextually relevant entities for, e.g., software repair tasks.
• Call Graph, Control/Data-Dependence Graphs, and Structural Semantic Graphs: Directed graphs capturing entities (classes, functions, attributes) and edges for structural, call, and import dependencies support context construction, as in Code Context Graphs (CCGs) and the Repository Structural Semantic Graph (RSSG) (Liu et al., 20 Jul 2025, Liu et al., 2024).
• Chunk- or Sliding Window–Based Context: The repository is partitioned into fixed-size chunks or snippets. Context is built by retrieving top-k most similar (according to lexical, embedding, or hybrid metrics) units for a given query (Zhang et al., 2023, Shrivastava et al., 2023).

2. Methodologies for Context File Extraction and Construction

Prominent techniques for building repository-level context files include:

• Heuristic Extraction and Ranking: Discrete “prompt proposals” select files/types (current file, parent, import, sibling, similar-named, etc.) and context types (identifiers, signatures, methods, bodies) using AST parsing and repository metadata. Ranking is via classifier-based or similarity-based scoring, sometimes learning a proposal selection model (Shrivastava et al., 2022, Shrivastava et al., 2023).
• Retrieval-Augmented Generation (RAG) Pipelines: Context is constructed by retrieving relevant snippets using sparse (BM25, token overlap), dense (embedding similarity), or hybrid metrics, often in multiple retrieval/generation rounds (Zhang et al., 2023, Liang et al., 2024, Pan et al., 2024).
• Graph-Based Retrieval and Pruning: Semantic graphs (e.g., RSG, RSSG, CCG) enable subgraph expansion (BFS, meta-path filtering), GNN-based link prediction, or one-hop neighborhood extraction to identify contextual code (Phan et al., 2024, Liu et al., 20 Jul 2025, Liu et al., 2024, Zhang et al., 2024).
• Hybrid and Hierarchical Techniques: Function-level representations extract relevant nodes and prune code below the header/signature for all but top-k or top-p relevant hits, preserving topological file dependencies in prompt assembly, as in Hierarchical Context Pruning (HCP) (Zhang et al., 2024).
• Dynamic and Typestate-Guided Context: For dynamic program repair, a typestate automaton guides tracing and extraction of only those statements on error-propagation paths, dramatically shrinking the input while encoding critical memory-management state (Cheng et al., 23 Jun 2025).
• User Behavior and Symbol-Aware Retrieval: In production coding assistants, context modules capture recently browsed/edited snippets, lazily-index similar code, and expose critical symbol signatures, ordered by importance or recency (Guan et al., 2024).

3. Repository-Level Context File Design Patterns

Context files differ by their structuring, serialization, and prompt integration:

• Inline Code Blocks: Multiple retrieved code snippets (tagged by file or function) are concatenated, with context windows marshalled before/in the middle/after the target hole depending on model/instruction (Zhang et al., 2023, Guan et al., 2024).
• Hierarchical/Structure-Preserving Serialization: Entities, call chains, and class hierarchies are serialized as tree-like code/narrative blocks, preserving parent/child/type relationships for LLM attention (Liu et al., 20 Jul 2025).
• Metadata and Path Annotation: For software repair, each function’s context block includes file path, signature, entity path (through the knowledge graph), and source line range, preceding the code excerpt (Yang et al., 27 Mar 2025).
• Symbolic and Weighted Rationale: Context is partitioned into blocks by type (e.g., symbol definitions, rationale/in-scope methods/packages/classes, analogy context/exemplars, user behavior blocks) and ordered by decreasing priority or relevance score (Guan et al., 2024, Liang et al., 2024).
• Control Tokens and Dynamic Filtering: Advanced pipelines (e.g., RepoShapley) include explicit KEEP/DROP tokens for each chunk, learned from Shapley-value supervision to determine the coalition-optimal context subset for decoding (Huo et al., 6 Jan 2026).
• Minimal Policy Contexts: AGENTS.md or CLAUDE.md are manually or LLM-generated markdown files summarizing repo structure, commands, and conventions for agentic systems, to steer LLM or coding agent behavior (Gloaguen et al., 12 Feb 2026).

4. Evaluation Paradigms and Empirical Findings

Repository-level context file design is validated through quantitative experiments:

• Completion Accuracy: Success rates, exact match (EM), edit similarity (ES), chrF, and unit test pass@k are measured on benchmarks like RepoEval, CrossCodeEval, CoderEval, DevEval, and SWE-bench.
• Ablation Studies: Experiments probe the value of different context types (function/type/class, symbol vs. similar code, chunk vs. file retrieval), context size ($k$, $p$), ordering, and pruning levels (Zhang et al., 2024, Shrivastava et al., 2023, Yusuf et al., 8 Oct 2025).
• Latency and Efficiency: Systems such as ContextModule and RepoFuse report retrieval latency (sub-200 ms for production) and index sizes, showing that lightweight indexing and hierarchical or truncated selection enable scalable online inference (Guan et al., 2024, Liang et al., 2024).
• Interaction Effects: Empirical results highlight non-additive utility among context chunks. Shapley-value–supervised filtering (RepoShapley) measurably reduces harmful or redundant input, boosting completion accuracy beyond previous selection heuristics (Huo et al., 6 Jan 2026).
• Real-World Integration: Context file techniques have been deployed in large-scale IDE plug-ins and used in agentic/coding assistant harnesses, with varying degrees of improvement depending on the artifact’s specificity and relevance (Guan et al., 2024, Gloaguen et al., 12 Feb 2026).

The following table summarizes representative context methodologies and main empirical outcomes:

Approach / Paper	Context File Composition	Max. Reported Gain
RLPG (Shrivastava et al., 2022)	Heuristic prompt proposals	+16–36% rel. improvement
RepoScope (Liu et al., 20 Jul 2025)	RSSG, multi-view serialized	+17–36% relative pass@1
CatCoder (Pan et al., 2024)	Code + type context, static	+17% (Java), +7% (Rust) pass@k
RepoFuse (Liang et al., 2024)	Rationale + analogy dual-context	41→60% EM, +27% inference speed
HCP (Zhang et al., 2024)	Function graph, hierarchical	+5–8 abs. EM, –80% prompt length
RepoShapley (Huo et al., 6 Jan 2026)	Shapley-based coalition filter	+4–5% (ES/EM) over best baselines
AGENTS.md (Gloaguen et al., 12 Feb 2026)	Markdown config for agents	±0 to –2% (LLM-gen); +4% (human)

5. Challenges, Limitations, and Best Practices

Despite their potential, repository-level context files introduce technical and practical challenges:

• Selection and Sufficiency: Over- or under-retrieval can degrade model performance by introducing noise or omitting required context (Tao et al., 6 Oct 2025, Kovrigin et al., 2024). Automatic sufficiency detection—knowing when enough has been retrieved—remains unresolved, with reasoning-augmented agents showing only modest gains (Kovrigin et al., 2024).
• Context Interaction: Chunks may provide utility only in specific combinations; naive scoring is inadequate. Structured approaches (Shapley, coalition modeling) outperform additive or isolated scoring (Huo et al., 6 Jan 2026).
• Scalability and Latency: Context file size often exceeds LLM context windows. Techniques such as hierarchical/pruned selection (Zhang et al., 2024), dual-context truncation (Liang et al., 2024), or statically bounded search (Liu et al., 20 Jul 2025) address this, but require careful tuning per language/model.
• Generalizability and Maintenance: Static-analyzer–based methods can support multiple languages with AST adaptation (Yang et al., 27 Mar 2025), but dynamic features and updating on fast-evolving repos add system complexity.
• Agent Integration: Human- or LLM-written markdown context files (AGENTS.md) can improve tool usage but risk degrading agent performance if overloaded with extraneous detail. Minimal, essential policy content is recommended (Gloaguen et al., 12 Feb 2026).

Best practices include:

• Preserving repository structure (e.g., via hierarchical or tree-based serialization) to maximize LLM compatibility with global invariants (Liu et al., 20 Jul 2025, Zhang et al., 2024).
• Hybrid retrieval (combining sparse, dense, graph, and symbolic sources) to trade off precision/recall (Pan et al., 2024, Tao et al., 6 Oct 2025).
• Truncating and ordering snippets by relevance, introducing coalition-aware selection when possible (Liang et al., 2024, Yusuf et al., 8 Oct 2025, Huo et al., 6 Jan 2026).
• Context window optimization and prompt budgeting to meet latency, interactiveness, and model window constraints (Guan et al., 2024, Zhang et al., 2024).

6. Impact on Repository-Aware Code Generation, Editing, and Repair

The adoption of repository-level context files catalyzes advances in multiple software engineering tasks:

• Code Completion and Generation: Enriched context enables code models to resolve symbol references, maintain API and type consistency, and generate repository-compliant code, yielding above-baseline exact match and unit-test pass rates in large repo benchmarks (Zhang et al., 2023, Liu et al., 20 Jul 2025, Zhang et al., 2024).
• Code Editing and Repair: Knowledge-graph– and typestate-guided context files localize and repair multi-hop or interprocedural bugs more precisely and at lower cost (Yang et al., 27 Mar 2025, Cheng et al., 23 Jun 2025).
• Vulnerability Detection: Repository-level context (e.g., relevant function dependencies) enhances both explainability and detection of complex, interprocedural vulnerabilities missed by function-local models (Wen et al., 2024).
• Agentic and Automated Development Tools: Repository context files supply agent-based frameworks with build/test/run commands, code navigation instructions, and task-specific invariants, expanding LLM capabilities for end-to-end development tasks (Gloaguen et al., 12 Feb 2026).

7. Future Research Directions

Repository-level context files remain an active area of research, with several salient directions:

• End-to-End Learnable Retrieval: Integrate retriever selection, context induction, and coalition modeling into a joint, trainable system, closing the loop between context extraction and generative performance (Tao et al., 6 Oct 2025, Huo et al., 6 Jan 2026).
• Functional/Behavioral Evaluation: Move beyond text-level metrics to environment-based or test-driven measures of context sufficiency and utility in code completion and repair (Pan et al., 2024, Yang et al., 27 Mar 2025).
• Multi-Modal and Interactive Contexts: Allow for incremental or user-guided context augmentation, harness agent-user interaction and runtime traces as context for more dynamic tasks (Kovrigin et al., 2024).
• Scaling to Large, Multilingual Repositories: Generalize approaches for non-Python/Java ecosystems, adapt to mixed-language stacks, and handle evolving projects with continuous codebase indexing.
• Privacy and Deployment: Develop privacy-preserving retrieval and context construction suitable for industrial and on-prem environments (Tao et al., 6 Oct 2025).

In sum, repository-level context files formalize and operationalize the broader program environment, enabling code models and agents to reason with the global context necessary for robust repository-level automation. Their ongoing evolution is central to the progress of AI-powered software engineering.