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WiseSpec addresses a critical bottleneck in LLM-based code generation by shifting focus from tool augmentation to the refinement of task requirements themselves. By automatically constructing and iteratively improving structured specifications, this framework significantly enhances the accuracy of repository-level development tasks. This approach represents a meaningful evolution in software engineering workflows where AI agents act as intelligent requirement engineers rather than just raw coders.
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WiseSpec: Requirements-Driven Agents for Code Generation

arXiv2 min read

arXiv:2609.00568v1 Announce Type: cross 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.

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.

Software Engineering (cs.SE); Artificial Intelligence (cs.AI)

arXiv:2609.00568 [cs.SE]

(or arXiv:2609.00568v1 [cs.SE] for this version)

https://doi.org/10.48550/arXiv.2609.00568 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 237 words

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