EnvGraph improves executable repository-level code generation by jointly modeling external dependencies and internal references through a dual-layer environment representation and targeted iterative alignment.
RepoGraph: Enhancing AI software engineering with repository-level code graph.arXiv preprint arXiv:2410.14684, 2024
10 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 10representative citing papers
InlineCoder reframes repository-level code generation as function-level coding by using a draft anchor to inline the target function into its call graph for upstream usage and downstream dependency context.
Holmes is a multimodal multi-agent system using a hierarchical Retrieve-Explore-Reason architecture to automate root cause analysis of mobile crashes, achieving 87.6% function-level accuracy and 98% time reduction on real WeChat data.
Probe-and-refine tuning refines AGENTS.md files using synthetic probes and improves coding agent resolve rate on SWE-bench Verified from 28.3% to 33.0% mainly by increasing coverage rather than per-patch precision.
TICoder improves repository-level code generation by 11.52% over prior methods through test-driven planning and implementation-aware code reuse on standard benchmarks.
abcoder-ts-parser builds reliable function-level code indexes for large TypeScript repositories significantly faster by using the compiler's native AST and semantic resolution instead of per-symbol language server calls.
Agent-CoEvo is a multi-agent LLM framework that coevolves code patches and test patches to resolve repository-level issues, outperforming fixed-test baselines on SWE-bench Lite and SWT-bench Lite.
An empirical study finds that injecting call/inheritance topology as comments improves LLM code agent localization by 2.2pp, shortens trajectories by 1.6 rounds, and halves run-to-run variance on medium repositories via a deterministic anchoring effect.
Ablation study finds that a structural codebase index improves localization and resolve rates in coding agents on two SWE benchmarks without raising per-cell cost.
citing papers explorer
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Toward Executable Repository-Level Code Generation via Environment Alignment
EnvGraph improves executable repository-level code generation by jointly modeling external dependencies and internal references through a dual-layer environment representation and targeted iterative alignment.
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In Line with Context: Repository-Level Code Generation via Context Inlining
InlineCoder reframes repository-level code generation as function-level coding by using a draft anchor to inline the target function into its call graph for upstream usage and downstream dependency context.
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Holmes: Multimodal Agentic Diagnosis for Mixed-Language Mobile Crashes at Industrial Scale
Holmes is a multimodal multi-agent system using a hierarchical Retrieve-Explore-Reason architecture to automate root cause analysis of mobile crashes, achieving 87.6% function-level accuracy and 98% time reduction on real WeChat data.
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Probe-and-Refine Tuning of Repository Guidance for Coding Agents
Probe-and-refine tuning refines AGENTS.md files using synthetic probes and improves coding agent resolve rate on SWE-bench Verified from 28.3% to 33.0% mainly by increasing coverage rather than per-patch precision.
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TICoder: A Repository-Level Code Generation Framework with Test-Driven Planning and Implementation-Aware Reuse
TICoder improves repository-level code generation by 11.52% over prior methods through test-driven planning and implementation-aware code reuse on standard benchmarks.
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TypeScript Repository Indexing for Code Agent Retrieval
abcoder-ts-parser builds reliable function-level code indexes for large TypeScript repositories significantly faster by using the compiler's native AST and semantic resolution instead of per-symbol language server calls.
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Beyond Fixed Tests: Repository-Level Issue Resolution as Coevolution of Code and Behavioral Constraints
Agent-CoEvo is a multi-agent LLM framework that coevolves code patches and test patches to resolve repository-level issues, outperforming fixed-test baselines on SWE-bench Lite and SWT-bench Lite.
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How Much Static Structure Do Code Agents Need? A Study of Deterministic Anchoring
An empirical study finds that injecting call/inheritance topology as comments improves LLM code agent localization by 2.2pp, shortens trajectories by 1.6 rounds, and halves run-to-run variance on medium repositories via a deterministic anchoring effect.
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Code Isn't Memory: A Structural Codebase Index Inside a Coding Agent
Ablation study finds that a structural codebase index improves localization and resolve rates in coding agents on two SWE benchmarks without raising per-cell cost.
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