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

• URL: http://arxiv.org/abs/2602.13987v1
• Date: Sun, 15 Feb 2026 04:47:58 GMT
• Title: ATTest: Agent-Driven Tensor Testing for Deep Learning Library Modules
• Authors: Zhengyu Zhan, Ye Shang, Jiawei Liu, Chunrong Fang, Quanjun Zhang, Zhenyu Chen,
• Abstract summary: Unit testing of Deep Learning (DL) libraries is challenging due to complex numerical semantics and implicit tensor constraints.<n>This paper proposes ATTest, an agent-driven testing framework for module-level unit test generation.
• Score: 19.355376741404267
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

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