TENET: AI Framework for Test-Driven Repository-Level Code Generation
A recent paper published on arXiv (2509.24148) presents TENET, a framework designed for repository-level code generation within the Test-Driven Development (TDD) model. This framework tackles the challenge of implementing TDD with tests created by developers through three main strategies: specification enhancement, which identifies a small yet representative subset of tests from extensive suites; retrieval augmentation, which utilizes tests to assist in reasoning and retrieving context; and test-driven refinement, which analyzes test feedback for continuous improvement. TENET's goal is to transition developers from writing code manually to establishing tests as executable specifications, with AI agents handling code synthesis. This announcement on arXiv also indicates a revision of the work, emphasizing its significance at the crossroads of AI and software development, particularly in automating code generation.
Key facts
- TENET is an agentic framework for repository-level code generation under TDD.
- It includes a test harness mechanism to select a concise test suite maximizing diversity.
- The framework addresses specification enhancement, retrieval augmentation, and test-driven refinement.
- The paper is available on arXiv with ID 2509.24148.
- The announcement type is 'replace-cross', indicating a revision.
- TENET leverages Large Language Models (LLMs) for code synthesis.
- The approach shifts developers from manual coding to defining tests as executable specifications.
- The paper was announced on arXiv.
Entities
Institutions
- arXiv