CodeSpec: Dual Executable Specifications for Agentic Long-Horizon Feature Development

2026-07-29

AI Summary

This work addresses the challenge that large language model–based code agents struggle to construct complete, consistent, and verifiable cross-component functional chains over extended development cycles, often leading to misalignment between design and implementation. To mitigate this, the paper introduces CodeSpec, a novel approach featuring a dual-executable specification mechanism: it generates functional chains through semantic-architecture pairing and compiles them into complementary architectural and behavioral specifications, thereby ensuring design completeness and implementation consistency. Integrating evidence-based functional chain construction with a collaborative large-model development framework, CodeSpec achieves pass rates of 70.7%, 55.0%, and 49.9% on FeatureBench using DeepSeek-V4-Pro, significantly outperforming baselines such as Claude Code, and demonstrates strong generalization on NL2Repo-Bench.

Abstract

LLM-based code agents have advanced repository-level software development through iterative interaction with codebases and tools. However, feature development requires integrating new behaviors into existing architectures through coherent cross-component functional chains. Existing agents typically derive such chains through free-form reasoning, often producing unreliable feature designs with incomplete functional chains. Moreover, textual designs are difficult to verify and enforce, making it challenging to maintain design-implementation consistency throughout long-horizon development. We propose CodeSpec, a dual executable specification method for repository-level feature development. It builds reliable functional chains from evidence pairing sub-requirement semantics with repository architectures, then compiles them into complementary architecture and behavior specifications that check chain completeness and correctness while preserving design-implementation consistency over long interactions. On FeatureBench, which targets feature development in existing repositories, CodeSpec achieves 70.7%, 55.0%, and 49.9% pass rates under DeepSeek-V4-Pro, outperforming representative baselines such as Claude Code. Results on the repository generation benchmark NL2Repo-Bench further demonstrate its generalizability.

Problem

• feature development
• functional chains
• design-implementation consistency
• executable specifications
• long-horizon software development

Innovation

• executable specification
• functional chain
• design-implementation consistency
• repository-level development
• code agent