# ATTest: Agent-Driven Tensor Testing for Deep Learning Library Modules

arXiv: https://arxiv.org/abs/2602.13987 · HTML: https://mlsanity.com/paper/2602.13987/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 15 of its 19 references that are in the map.
- First posted on arXiv 15 Feb 2026.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 15 of 15)

- [Large Language Models are Few-shot Testers: Exploring LLM-based General Bug Reproduction](https://mlsanity.com/paper/2209.11515/) (2022)
- [ChatUniTest: A Framework for LLM-Based Test Generation](https://mlsanity.com/paper/2305.04764/) (2023)
- [TOGA: A Neural Method for Test Oracle Generation](https://mlsanity.com/paper/2109.09262/) (2021)
- [Pynguin: Automated Unit Test Generation for Python](https://mlsanity.com/paper/2202.05218/) (2022)
- [DocTer: Documentation Guided Fuzzing for Testing Deep Learning API Functions](https://mlsanity.com/paper/2109.01002/) (2021)
- [QualityFlow: An Agentic Workflow for Program Synthesis Controlled by LLM Quality Checks](https://mlsanity.com/paper/2501.17167/) (2025)
- [TestART: Improving LLM-based Unit Testing via Co-evolution of Automated Generation and Repair Iteration](https://mlsanity.com/paper/2408.03095/) (2024)
- [TestBench: Evaluating Class-Level Test Case Generation Capability of Large Language Models](https://mlsanity.com/paper/2409.17561/) (2024)
- [A Large-scale Empirical Study on Fine-tuning Large Language Models for Unit Testing](https://mlsanity.com/paper/2412.16620/) (2024)
- [TestSpark: IntelliJ IDEA's Ultimate Test Generation Companion](https://mlsanity.com/paper/2401.06580/) (2024)
- [Large Language Models for Unit Testing: A Systematic Literature Review](https://mlsanity.com/paper/2506.15227/) (2025)
- [TDFlow: Agentic Workflows for Test Driven Development](https://mlsanity.com/paper/2510.23761/) (2025)
- [Enhancing LLM's Ability to Generate More Repository-Aware Unit Tests Through Precise Contextual Information Injection](https://mlsanity.com/paper/2501.07425/) (2025)
- [Automatic Unit Test Generation for Deep Learning Frameworks based on API Knowledge](https://mlsanity.com/paper/2307.00404/) (2023)
- [Constraint-Guided Unit Test Generation for Machine Learning Libraries](https://mlsanity.com/paper/2510.09108/) (2025)

## Abstract (arXiv)

The unit testing of Deep Learning (DL) libraries is challenging due to complex numerical semantics and implicit tensor constraints. Traditional Search-Based Software Testing (SBST) often suffers from semantic blindness, failing to satisfy the constraints of high-dimensional tensors, whereas Large Language Models (LLMs) struggle with cross-file context and unstable code modifications. This paper proposes ATTest, an agent-driven tensor testing framework for module-level unit test generation. ATTest orchestrates a seven-stage pipeline, which encompasses constraint extraction and an iterative "generation-validation-repair" loop, to maintain testing stability and mitigate context-window saturation. An evaluation on PyTorch and TensorFlow demonstrates that ATTest significantly outperforms state-of-the-art baselines such as PynguinML, achieving an average branch coverage of 55.60% and 54.77%, respectively. The results illustrate how agent-driven workflows bridge the semantic gap in numerical libraries while ensuring auditable test synthesis. Source code: https://github.com/iSEngLab/ATTest.git