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AgileCoder: Dynamic Collaborative Agents for Software Development based on Agile Methodology

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arxiv 2406.11912 v2 pith:6WXX5NCI submitted 2024-06-16 cs.SE cs.AI

classification cs.SEcs.AI
keywords softwareagentsagilecoderdevelopmentcodeagentagilecodebase
verification ladder T0 review T1 audit T2 compute T3 formal
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Software agents have emerged as promising tools for addressing complex software engineering tasks. Existing works, on the other hand, frequently oversimplify software development workflows, despite the fact that such workflows are typically more complex in the real world. Thus, we propose AgileCoder, a multi agent system that integrates Agile Methodology (AM) into the framework. This system assigns specific AM roles - such as Product Manager, Developer, and Tester to different agents, who then collaboratively develop software based on user inputs. AgileCoder enhances development efficiency by organizing work into sprints, focusing on incrementally developing software through sprints. Additionally, we introduce Dynamic Code Graph Generator, a module that creates a Code Dependency Graph dynamically as updates are made to the codebase. This allows agents to better comprehend the codebase, leading to more precise code generation and modifications throughout the software development process. AgileCoder surpasses existing benchmarks, like ChatDev and MetaGPT, establishing a new standard and showcasing the capabilities of multi agent systems in advanced software engineering environments.

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Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Multi-Agent LLM Collaboration for Unit Test Generation via Human-Testing-Inspired Workflows

    cs.SE 2026-07 conditional novelty 6.0 of 10

    A three-agent LLM workflow plus a test-specialized knowledge graph generates unit tests that beat prior LLM and search-based tools on coverage and mutation score.

  2. Compiling Large Multi-Modal Requirement Documents into Runnable Software Systems: From an Agentic Test-Driven Perspective

    cs.SE 2026-02 conditional novelty 6.0 of 10

    ARC compiles DSL-based requirement documents into runnable web apps by generating interface tests top-down, then using those tests to gate bottom-up code generation, achieving higher GUI pass rates than baseline codin...

  3. Think Like an Engineer: A Neuro-Symbolic Collaboration Agent for Generative Software Requirements Elicitation and Self-Review

    cs.SE 2025-07 conditional novelty 6.0 of 10

    RequireCEG combines large language models with causal-effect graphs to elicit and self-review Gherkin requirements from natural language narratives, reporting improved quality, diversity, and consistency over baselines.

  4. Single-agent or Multi-agent Systems? Why Not Both?

    cs.MA 2025-05 conditional novelty 6.0 of 10

    On 15 agentic benchmarks, the accuracy advantage of multi-agent LLM systems over single-agent systems mostly disappears with stronger base models, and a hybrid single/multi-agent cascade improves accuracy and cuts cost.

  5. Cognitive Agents Powered by Large Language Models for Agile Software Project Management

    cs.SE 2025-08 reject novelty 4.0 of 10

    LLM agents acting as Agile roles produced plausible project artifacts in simulation, but the claimed improvements over human teams are unsupported because no comparison or validated metrics are provided.

  6. Knowledge Graph Based Repository-Level Code Generation

    cs.AI 2025-05 reject novelty 4.0 of 10

    A knowledge graph code retrieval pipeline is described, but its headline results come from an evaluation that skips the retrieval step and anchors context on the known target function.

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