arXiv cs.AI· Yiran Hu, Shanchao Liang, Nan Jiang, Yi Wu, Lin Tan·· 3 天前AI 评分32
TENET：面向仓库级代码生成的测试驱动开发框架
TENET: One Step Toward Test-Driven Development for Repository-Level Code Generation
AI 导读
TENET 是一个在测试驱动开发（TDD）范式下进行仓库级代码生成的智能体框架，包含测试套件选择、检索调试工具集和基于反思的迭代改进流程。在 Claude Sonnet 4 上，TENET 于 RepoCod 和 RepoEval 分别达到 69.08% 和 81.77% Pass@1，较最强基线提升 9.49 和 2.17 个百分点。
正文 · 原文
Abstract:Test-Driven Development (TDD) is a widely adopted practice that requires developers to create and execute tests alongside implementation. With recent advances in Large Language Models (LLMs), developers can shift from manually writing the code to defining tests as executable specifications and delegating code synthesis to AI agents. However, enabling repository-level TDD under developer-written tests is challenging, requiring: (1) specification enhancement: identifying a concise yet representative test subset from large suites with rich task semantics; (2) retrieval augmentation: using tests to guide reasoning and context retrieval; and (3) test-driven refinement: interpreting test feedback for iterative improvement. We propose TENET, an agentic framework for repository-level code generation under the TDD paradigm. TENET includes: (1) a test harness mechanism that selects a concise test suite to maximize diversity of the target usage scenarios; (2) a tailored agent toolset for efficient retrieval and debugging; and (3) a reflection-based refinement workflow that iteratively analyzes failures and updates implementations. TENET consistently outperforms the strongest baselines across backbones, achieving 69.08% and 81.77% Pass@1 on RepoCod and RepoEval with Claude Sonnet 4, improving by 9.49 and 2.17 percentage points, respectively. Additionally, we present the first systematic study of how test suite characteristics influence LLM agent performance in TDD settings.
| Comments: | Accepted at the 37th IEEE International Symposium on Software Reliability Engineering (ISSRE 2026) |
| Subjects: | Software Engineering (cs.SE); Artificial Intelligence (cs.AI) |
| ACM classes: | D.2.5; I.2.2 |
| Cite as: | arXiv:2509.24148 [cs.SE] |
| (or arXiv:2509.24148v5 [cs.SE] for this version) | |
| https://doi.org/10.48550/arXiv.2509.24148 arXiv-issued DOI via DataCite |
Submission history
From: Yiran Hu [view email]
[v1]
Mon, 29 Sep 2025 00:53:16 UTC (2,012 KB)
[v2]
Tue, 30 Sep 2025 04:05:32 UTC (2,012 KB)
[v3]
Sun, 12 Jul 2026 23:05:42 UTC (806 KB)
[v4]
Thu, 13 Aug 2026 19:04:55 UTC (736 KB)
[v5]
Sun, 11 Oct 2026 18:23:18 UTC (733 KB)
来源：arXiv cs.AI · arxiv.org