CodeSpec: Dual Executable Specifications for Agentic Long-Horizon Feature Development
On the map: LLM agents and tool use → Software engineering coding agents
Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 9 of its 21 references that are in the map. First posted on arXiv 29 Jul 2026.
Builds on
9 of 21 references.
Closest ancestors · 3
Same direction, newest first
- 2026FeatureBench: Benchmarking Agentic Coding for Complex Feature Development11 Feb
- 2024OpenHands: An Open Platform for AI Software Developers as Generalist Agents23 Jul
- 2023ChatDev: Communicative Agents for Software Development16 Jul
Other references · 6
- 2021Evaluating Large Language Models Trained on Codecited by 8,053
- 2021Program Synthesis with Large Language Modelscited by 3,509
- 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,193
- 2025FEA-Bench: A Benchmark for Evaluating Repository-Level Code Generation for Feature Implementationcited by 57
- 2025CodeIF-Bench: Evaluating Instruction-Following Capabilities of Large Language Models in Interactive Code Generationcited by 14
- 2026CodeMEM: AST-Guided Adaptive Memory for Repository-Level Iterative Code Generationcited by 2
Built on by
Citing papers per year: 2026: 2.
2 papers.
Similar, not cited
Close in wording, no citation either way
- 2025NL2Repo-Bench: Towards Long-Horizon Repository Generation Evaluation of Coding Agents
- 2026Large-scale Repository Engineering via Agent-Native Reusable Code Primitives
- 2026Towards Reliable ML Feature Engineering via Planning in Constrained-Topology of LLM Agents
- 2026RACE-Bench: A Reasoning-Augmented Benchmark for Repository-Level Code Agents on Feature Addition
- 2026E2E-SWE: Benchmarking LLMs on Building Working Codebases from Scratch
- 2026Repo0: Design-Driven Zero-to-All Code Generation
- 2026CodeTeam: An LLM-Powered Multi-Agent Framework for Repository-Level Code Generation
- 2026SWE-CI: Evaluating Agent Capabilities in Maintaining Codebases via Continuous Integration