mlsanityData to 7 Oct 2026

WiseSpec: Requirements-Driven Agents for Code Generation

arXiv 2609.00568 ↗First posted 1 Sep 2026cs.SE cs.AIcited by 0

On the map: LLM agents and tool use → Software engineering coding agents

Abstract (arXiv)

Code generation aims to automatically generate source code from task requirements and has attracted significant attention with the rapid advancement of large language models (LLMs). Despite remarkable progress, LLMs often struggle to generate correct code for complex software engineering tasks because task descriptions are frequently incomplete, ambiguous, or lack critical contextual information. Existing approaches primarily improve the capabilities of coding agents through more sophisticated tools, skills, and workflows, while largely overlooking the quality of the task requirements themselves. To address this limitation, we draw inspiration from software requirements engineering and propose WiseSpec, a novel requirements-driven agent framework for repository-level code generation. WiseSpec automatically constructs structured and information-rich requirements, assesses their quality through execution-based evaluation, and iteratively refines them to better guide code generation. Experimental results show that WiseSpec consistently outperforms all baselines, achieving an average improvement of 13.17% in %Resolved.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 8 of its 15 references that are in the map. First posted on arXiv 1 Sep 2026.

Builds on

8 of 15 references.

Closest ancestors · 3

Same direction, newest first

  1. 2026REAgent: Requirement-Driven LLM Agents for Software Issue Resolution8 Apr
  2. 2025Trae Agent: An LLM-based Agent for Software Engineering with Test-time Scaling31 Jul
  3. 2024SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering6 May

Other references · 5

  1. 2025Qwen3 Technical Reportcited by 8,195
  2. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,193
  3. 2025SWE-Bench Pro: Can AI Agents Solve Long-Horizon Software Engineering Tasks?cited by 254
  4. 2023On the Robustness of Code Generation Techniques: An Empirical Study on GitHub Copilotcited by 64
  5. 2025Aligning Requirement for Large Language Model's Code Generationcited by 4

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Similar, not cited

Close in wording, no citation either way

  1. 2025Requirements Development and Formalization for Reliable Code Generation: A Multi-Agent Vision
  2. 2025Towards Realistic Project-Level Code Generation via Multi-Agent Collaboration and Semantic Architecture Modeling
  3. 2025A Survey on Code Generation with LLM-based Agents
  4. 2026TraceDev: A Traceability-Driven Multi-agent Framework for Requirement-to-Code Development
  5. 2025NL2Repo-Bench: Towards Long-Horizon Repository Generation Evaluation of Coding Agents
  6. 2026On the Viability of Requirements Generation From Code: An Experience Report ↗
  7. 2025GraphCodeAgent: Dual Graph-Guided LLM Agent for Retrieval-Augmented Repo-Level Code Generation
  8. 2026E2E-SWE: Benchmarking LLMs on Building Working Codebases from Scratch