mlsanityData to 7 Oct 2026

CodePlan: Repository-level Coding using LLMs and Planning

arXiv 2309.12499 ↗First posted 21 Sep 2023cs.SEcited by 139

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Cited by 44 in LLM agents and tool use, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Software engineering activities such as package migration, fixing errors reports from static analysis or testing, and adding type annotations or other specifications to a codebase, involve pervasively editing the entire repository of code. We formulate these activities as repository-level coding tasks. Recent tools like GitHub Copilot, which are powered by Large Language Models (LLMs), have succeeded in offering high-quality solutions to localized coding problems. Repository-level coding tasks are more involved and cannot be solved directly using LLMs, since code within a repository is inter-dependent and the entire repository may be too large to fit into the prompt. We frame repository-level coding as a planning problem and present a task-agnostic framework, called CodePlan to solve it. CodePlan synthesizes a multi-step chain of edits (plan), where each step results in a call to an LLM on a code location with context derived from the entire repository, previous code changes and task-specific instructions. CodePlan is based on a novel combination of an incremental dependency analysis, a change may-impact analysis and an adaptive planning algorithm. We evaluate the effectiveness of CodePlan on two repository-level tasks: package migration (C#) and temporal code edits (Python). Each task is evaluated on multiple code repositories, each of which requires inter-dependent changes to many files (between 2-97 files). Coding tasks of this level of complexity have not been automated using LLMs before. Our results show that CodePlan has better match with the ground truth compared to baselines. CodePlan is able to get 5/6 repositories to pass the validity checks (e.g., to build without errors and make correct code edits) whereas the baselines (without planning but with the same type of contextual information as CodePlan) cannot get any of the repositories to pass them.

Key facts. Cited by 139 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Sep 2023.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 5; 2024: 44; 2025: 45; 2026: 45.

139 papers.

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  1. 2024SnapKV: LLM Knows What You are Looking for Before Generationcited by 848
  2. 2024A Survey on Large Language Models for Code Generationcited by 646
  3. 2024MInference 1.0: Accelerating Pre-filling for Long-Context LLMs via Dynamic Sparse Attentioncited by 375
  4. 2024RepairAgent: An Autonomous, LLM-Based Agent for Program Repaircited by 225
  5. 2023CacheGen: KV Cache Compression and Streaming for Fast Large Language Model Servingcited by 199
  6. 2024Data Engineering for Scaling Language Models to 128K Contextcited by 183
  7. 2024LLMs Can't Plan, But Can Help Planning in LLM-Modulo Frameworkscited by 178
  8. 2024HiAgent: Hierarchical Working Memory Management for Solving Long-Horizon Agent Tasks with Large Language Modelcited by 101
  9. 2024RepoGraph: Enhancing AI Software Engineering with Repository-level Code Graphcited by 88
  10. 2024VerilogCoder: Autonomous Verilog Coding Agents with Graph-based Planning and Abstract Syntax Tree (AST)-based Waveform Tracing Toolcited by 85
  11. 2025PlanGenLLMs: A Modern Survey of LLM Planning Capabilitiescited by 82
  12. 2024AgentGen: Enhancing Planning Abilities for Large Language Model based Agent via Environment and Task Generationcited by 80
38 more
  1. 2024DebugBench: Evaluating Debugging Capability of Large Language Modelscited by 74
  2. 2023Igniting Language Intelligence: The Hitchhiker's Guide From Chain-of-Thought Reasoning to Language Agentscited by 72
  3. 2023Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Codecited by 62
  4. 2024CodexGraph: Bridging Large Language Models and Code Repositories via Code Graph Databasescited by 62
  5. 2024Agents in Software Engineering: Survey, Landscape, and Visioncited by 60
  6. 2024Why Does the Effective Context Length of LLMs Fall Short?cited by 60
  7. 2024SCBench: A KV Cache-Centric Analysis of Long-Context Methodscited by 59
  8. 2024Alibaba LingmaAgent: Improving Automated Issue Resolution via Comprehensive Repository Explorationcited by 58
  9. 2025FEA-Bench: A Benchmark for Evaluating Repository-Level Code Generation for Feature Implementationcited by 57
  10. 2024What's Wrong with Your Code Generated by Large Language Models? An Extensive Studycited by 52
  11. 2024MASAI: Modular Architecture for Software-engineering AI Agentscited by 49
  12. 2025Code to Think, Think to Code: A Survey on Code-Enhanced Reasoning and Reasoning-Driven Code Intelligence in LLMscited by 47
  13. 2024AgileCoder: Dynamic Collaborative Agents for Software Development based on Agile Methodologycited by 45
  14. 2024You Name It, I Run It: An LLM Agent to Execute Tests of Arbitrary Projectscited by 45
  15. 2024REPOFUSE: Repository-Level Code Completion with Fused Dual Contextcited by 32
  16. 2026SWE-Lego: Pushing the Limits of Supervised Fine-tuning for Software Issue Resolvingcited by 30
  17. 2026LongCLI-Bench: A Preliminary Benchmark and Study for Long-horizon Agentic Programming in Command-Line Interfacescited by 29
  18. 2025RefactorBench: Evaluating Stateful Reasoning in Language Agents Through Codecited by 28
  19. 2023How Far Have We Gone in Vulnerability Detection Using Large Language Modelscited by 27
  20. 2023LLM-Assisted Code Cleaning For Training Accurate Code Generatorscited by 27
  21. 2024Octopus: On-device language model for function calling of software APIscited by 26
  22. 2025What to Retrieve for Effective Retrieval-Augmented Code Generation? An Empirical Study and Beyondcited by 22
  23. 2024M2rc-Eval: Massively Multilingual Repository-level Code Completion Evaluationcited by 21
  24. 2024Beyond pip install: Evaluating LLM Agents for the Automated Installation of Python Projectscited by 21
  25. 2025Retrieval-Augmented Code Generation: A Survey with Focus on Repository-Level Approachescited by 20
  26. 2025NExtLong: Toward Effective Long-Context Training without Long Documentscited by 19
  27. 2025debug-gym: A Text-Based Environment for Interactive Debuggingcited by 19
  28. 2026In Line with Context: Repository-Level Code Generation via Context Inliningcited by 18
  29. 2024Counting-Stars: A Multi-evidence, Position-aware, and Scalable Benchmark for Evaluating Long-Context Large Language Modelscited by 17
  30. 2025An Empirical Study of Retrieval-Augmented Code Generation: Challenges and Opportunitiescited by 17
  31. 2024Syzygy: Dual Code-Test C to (safe) Rust Translation using LLMs and Dynamic Analysiscited by 16
  32. 2024RepoHyper: Search-Expand-Refine on Semantic Graphs for Repository-Level Code Completioncited by 15
  33. 2024Quest: Query-centric Data Synthesis Approach for Long-context Scaling of Large Language Modelcited by 15
  34. 2024Beyond Functional Correctness: Investigating Coding Style Inconsistencies in Large Language Modelscited by 15
  35. 2025Migrating Code At Scale With LLMs At Googlecited by 14
  36. 2025ResearchCodeAgent: An LLM Multi-Agent System for Automated Codification of Research Methodologiescited by 14
  37. 2025RetroInfer: A Vector Storage Engine for Scalable Long-Context LLM Inferencecited by 13
  38. 2025From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligencecited by 13

50 most cited of 139 All 139 (JSON)