Skip to content

WiseSpec: Requirements-Driven Agents for Code Generation

Sep 2026 · 0 citations · 15 references
Computer Science

TL;DR

W WiseSpec is proposed, a novel requirements-driven agent framework for repository-level code generation that automatically constructs structured and information-rich requirements, assesses their quality through execution-based evaluation, and iteratively refines them to better guide code generation.

Abstract

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.

View source

Similar papers

Preprint Aug 2026

TDD-Agent: Test-Driven Reasoning for Code Generation

TDD-Agent is introduced, which operationalizes the test-driven development paradigm for code generation and improves not only code correctness but also the effectiveness of the generated tests, yielding higher pass rates, coverage, and mutation scores, suggesting that tests can serve as evolving reasoning artifacts rat...

Hong Yu, Ke-Fan Li, Jia-Kun Li et al. · 2 citations
Sep 2026

KGMACG: Knowledge-Guided Multi-Agent Orchestration for Scalable Application-Level Code Generation

KGMACG is evaluated on three industrial-scale case studies against six state-of-the-art multi-agent baselines: MetaGPT, AutoGen, CAMEL, CrewAI, ChatDev and CodeAgent and indicates that KGMACG advances the automation of application-level software development.

Bo Yang, Xiao Zhang, Wei-Song Sun et al. · 0 citations
#software testing Preprint Aug 2026

Repo0: Design-Driven Zero-to-All Code Generation

Repo0 is presented, a continuous structural evolution framework for zero-to-all code generation that maintains an explicit architectural state instantiated as a Dual-Directed-Acyclic-Graph (Dual-DAG), consisting of a requirement-level DAG, a component-level DAG, and their alignment relation.

Si-Lin Chen, Haoyi Teng, Xiao-Dong Gu et al. · 4 citations
#computer vision Review Aug 2026

REFINE: A Multi-Agent LLM Approach for Evidence-Guided Code Refactoring

This work introduces REFINE (Refactoring with Evidence-aware Flow for Integrated ageNtic Execution), a tool-agnostic, evidence-aware multi-agent approach for generating Java file-level refactoring candidates that achieves a higher median code-smell reduction with smaller edits and fewer public-method removals.

Muhammad Waseem, Aakash Ahmad, Pekka Abrahamsson · 0 citations
#natural language process... Preprint Sep 2026

MTAC-IFBench: Benchmarking Instruction-Following in Multi-Turn Agentic Coding

Recently, the rapid development of large language models (LLMs) has reshaped software engineering by enabling autonomous code agents that plan, execute, and utilize external tools iteratively to tackle complex tasks. Beyond achieving functional correctness, these agents must faithfully follow process instructions and c...

Bo-Si Wen, Cun-Xiang Wang, Jia-Yi Gui et al. · 0 citations
Book Open access Oct 2026

From Code to Knowledge: Enhancing Coding Agents via Reverse-Engineered Software Knowledge

Despite advances in code retrieval and static analysis, LLM-based coding agents rarely construct or consult structured representations of a codebase's architectural design, domain model, or behavioral patterns, the very knowledge that human developers depend on when making nontrivial changes. This absence of explicit k...

Yi-Ran Zhang, Yi-Fei Ge, Xiao-Xia Liu · 0 citations

Related blog posts

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.