mlsanityData to 7 Oct 2026

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

arXiv 2607.26777 ↗First posted 29 Jul 2026cs.SEcited by 2

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

  1. 2026FeatureBench: Benchmarking Agentic Coding for Complex Feature Development11 Feb
  2. 2024OpenHands: An Open Platform for AI Software Developers as Generalist Agents23 Jul
  3. 2023ChatDev: Communicative Agents for Software Development16 Jul

Other references · 6

  1. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  2. 2021Program Synthesis with Large Language Modelscited by 3,509
  3. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,193
  4. 2025FEA-Bench: A Benchmark for Evaluating Repository-Level Code Generation for Feature Implementationcited by 57
  5. 2025CodeIF-Bench: Evaluating Instruction-Following Capabilities of Large Language Models in Interactive Code Generationcited by 14
  6. 2026CodeMEM: AST-Guided Adaptive Memory for Repository-Level Iterative Code Generationcited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2025NL2Repo-Bench: Towards Long-Horizon Repository Generation Evaluation of Coding Agents
  2. 2026Large-scale Repository Engineering via Agent-Native Reusable Code Primitives
  3. 2026Towards Reliable ML Feature Engineering via Planning in Constrained-Topology of LLM Agents
  4. 2026RACE-Bench: A Reasoning-Augmented Benchmark for Repository-Level Code Agents on Feature Addition
  5. 2026E2E-SWE: Benchmarking LLMs on Building Working Codebases from Scratch
  6. 2026Repo0: Design-Driven Zero-to-All Code Generation
  7. 2026CodeTeam: An LLM-Powered Multi-Agent Framework for Repository-Level Code Generation
  8. 2026SWE-CI: Evaluating Agent Capabilities in Maintaining Codebases via Continuous Integration