WiseSpec: Requirements-Driven Agents for Code Generation
- URL: http://arxiv.org/abs/2609.00568v1
- Date: Tue, 01 Sep 2026 02:04:16 GMT
- Title: WiseSpec: Requirements-Driven Agents for Code Generation
- Abstract summary: WiseSpec is a requirements-driven agent framework for repository-level code generation.<n>We show that WiseSpec consistently outperforms all baselines, achieving an average improvement of 13.17% in %Resolved.
- Score: 3.1580161263510855
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
Related papers
- SpecCoder: Specification-Aware Code Generation with Curriculum Dual-Task Reinforcement Learning [30.588079618949447]
SpecCoder is a specification-aware two-stage training framework for code generation.<n>It first employs specification-guided SFT to train GRPs to derive structured specification analyses and generate code conditioned on them.<n>It then introduces curriculum dual-taskO, which jointly optimize specification-guided generation and discrimination to encourage stronger correspondence between specifications and code behavior.
arXiv Detail & Related papers (2026-09-05T11:52:54Z) - ClarifyCodeBench: Evaluating LLMs on Clarifying Ambiguous Requirements for Code Generation [54.788849448970154]
We introduce ClarifyCodeBench, a novel interactive benchmark for evaluating Large Language Models' capability in resolving requirement ambiguity.<n>We conduct a systematic evaluation of six state-of-the-art LLMs using ClarifyCodeBench.<n>Our empirical results yield three critical insights: 1) Capability Decoupling: Strong code generation performance does not inherently translate to effective requirement clarification; 2) The Reasoning Paradox: While increased computational thinking enhances code correctness, it yields marginal gains in identifying ambiguities.
arXiv Detail & Related papers (2026-07-01T09:58:59Z) - REAgent: Requirement-Driven LLM Agents for Software Issue Resolution [35.30650311049838]
Issue resolution aims to automatically generate patches from given issue descriptions.<n>We propose REAgent, a requirement-driven agent framework to guide patch generation.
arXiv Detail & Related papers (2026-04-08T09:22:30Z) - Requirements Development and Formalization for Reliable Code Generation: A Multi-Agent Vision [45.59678433715798]
We envision the first multi-agent framework for reliable code generation based on textscrequirements textscdevelopment and textscformalization, named textscReDeFo.<n>The core of textscReDeFo is the use of formal specifications to bridge the gap between potentially ambiguous natural language requirements and precise executable code.
arXiv Detail & Related papers (2025-08-26T04:45:04Z) - Uncovering Systematic Failures of LLMs in Verifying Code Against Natural Language Specifications [0.6813925418351435]
Large language models (LLMs) have become essential tools in software development, widely used for requirements engineering, code generation and review tasks.<n>In this paper, we uncover a systematic failure of LLMs in evaluating whether code aligns with natural language requirements.<n>Our results reveal that LLMs frequently misclassify correct code implementations as either not satisfying requirements'' or containing potential defects.
arXiv Detail & Related papers (2025-08-17T13:07:26Z) - MERA Code: A Unified Framework for Evaluating Code Generation Across Tasks [56.34018316319873]
We propose MERA Code, a benchmark for evaluating code for the latest code generation LLMs in Russian.<n>This benchmark includes 11 evaluation tasks that span 8 programming languages.<n>We evaluate open LLMs and frontier API models, analyzing their limitations in terms of practical coding tasks in non-English languages.
arXiv Detail & Related papers (2025-07-16T14:31:33Z) - AGENTIF: Benchmarking Instruction Following of Large Language Models in Agentic Scenarios [51.46347732659174]
Large Language Models (LLMs) have demonstrated advanced capabilities in real-world agentic applications.<n>AgentIF is the first benchmark for systematically evaluating LLM instruction following ability in agentic scenarios.
arXiv Detail & Related papers (2025-05-22T17:31:10Z) - CodeIF: Benchmarking the Instruction-Following Capabilities of Large Language Models for Code Generation [20.013757490442064]
We introduce CodeIF, the first benchmark designed to assess the abilities of Large Language Models (LLMs) to adhere to task-oriented instructions.<n>CodeIF encompasses a broad range of tasks, including function synthesis, algorithmic instructions, and code explanation.<n>We conduct extensive experiments with LLMs, analyzing their strengths and limitations in meeting the demands of these tasks.
arXiv Detail & Related papers (2025-02-26T14:19:49Z) - Codev-Bench: How Do LLMs Understand Developer-Centric Code Completion? [60.84912551069379]
We present the Code-Development Benchmark (Codev-Bench), a fine-grained, real-world, repository-level, and developer-centric evaluation framework.
Codev-Agent is an agent-based system that automates repository crawling, constructs execution environments, extracts dynamic calling chains from existing unit tests, and generates new test samples to avoid data leakage.
arXiv Detail & Related papers (2024-10-02T09:11:10Z) - MMAU: A Holistic Benchmark of Agent Capabilities Across Diverse Domains [54.117238759317004]
Massive Multitask Agent Understanding (MMAU) benchmark features comprehensive offline tasks that eliminate the need for complex environment setups.
It evaluates models across five domains, including Tool-use, Directed Acyclic Graph (DAG) QA, Data Science and Machine Learning coding, Contest-level programming and Mathematics.
With a total of 20 meticulously designed tasks encompassing over 3K distinct prompts, MMAU provides a comprehensive framework for evaluating the strengths and limitations of LLM agents.
arXiv Detail & Related papers (2024-07-18T00:58:41Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.