arXiv (OAI Expanded)
open access
DiffuTester: Accelerating Unit Test Generation for Diffusion LLMs via Mining Structural Pattern
Diffusion large language models (dLLMs) enable parallel generation and are promising for unit test generation (UTG), where efficient and large-scale automated testing is essential in software development. Despite this advantage, their application to UTG is still constrained by a clear trade-off betw
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Large Language Models for Unit Test Generation: Achievements, Challenges, and Opportunities#1117283
- Simple Diffusion Language Models Are More Effective Few-Step Generators Than Reported#1119020
- Domain-Adapted Diffusion Models for Conditional Independence Testing#1118202
- From Generation to Discovery: Diffusion Mutation Kernels for Circuit and Physical Design#780949
- From Generation to Discovery: Diffusion Mutation Kernels for Circuit and Physical Design#779574
Record · ID 1070714
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive