Improving Deep Learning Framework Testing with Model-Level Metamorphic Testing

• URL: http://arxiv.org/abs/2507.04354v1
• Date: Sun, 06 Jul 2025 11:38:14 GMT
• Title: Improving Deep Learning Framework Testing with Model-Level Metamorphic Testing
• Authors: Yanzhou Mu, Juan Zhai, Chunrong Fang, Xiang Chen, Zhixiang Cao, Peiran Yang, Kexin Zhao, An Guo, Zhenyu Chen,
• Abstract summary: Deep learning (DL) frameworks are essential to DL-based software systems, and framework bugs may lead to substantial disasters.<n>Researchers adopt DL models or single interfaces as test inputs and analyze their execution results to detect bugs.<n> floating-point errors, inherent randomness, and the complexity of test inputs make it challenging to analyze execution results effectively.
• Score: 19.880543046739252
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning (DL) frameworks are essential to DL-based software systems, and framework bugs may lead to substantial disasters, thus requiring effective testing. Researchers adopt DL models or single interfaces as test inputs and analyze their execution results to detect bugs. However, floating-point errors, inherent randomness, and the complexity of test inputs make it challenging to analyze execution results effectively, leading to existing methods suffering from a lack of suitable test oracles. Some researchers utilize metamorphic testing to tackle this challenge. They design Metamorphic Relations (MRs) based on input data and parameter settings of a single framework interface to generate equivalent test inputs, ensuring consistent execution results between original and generated test inputs. Despite their promising effectiveness, they still face certain limitations. (1) Existing MRs overlook structural complexity, limiting test input diversity. (2) Existing MRs focus on limited interfaces, which limits generalization and necessitates additional adaptations. (3) Their detected bugs are related to the result consistency of single interfaces and far from those exposed in multi-interface combinations and runtime metrics (e.g., resource usage). To address these limitations, we propose ModelMeta, a model-level metamorphic testing method for DL frameworks with four MRs focused on the structure characteristics of DL models. ModelMeta augments seed models with diverse interface combinations to generate test inputs with consistent outputs, guided by the QR-DQN strategy. It then detects bugs through fine-grained analysis of training loss/gradients, memory/GPU usage, and execution time.

Related papers

• LlamaRestTest: Effective REST API Testing with Small Language Models [50.058600784556816]
  We present LlamaRestTest, a novel approach that employs two custom Large Language Models (LLMs) to generate realistic test inputs.<n>We evaluate it against several state-of-the-art REST API testing tools, including RESTGPT, a GPT-powered specification-enhancement tool.<n>Our study shows that small language models can perform as well as, or better than, large language models in REST API testing.
  arXiv  Detail & Related papers  (2025-01-15T05:51:20Z)
• Context-Aware Testing: A New Paradigm for Model Testing with Large Language Models [49.06068319380296]
  We introduce context-aware testing (CAT) which uses context as an inductive bias to guide the search for meaningful model failures. We instantiate the first CAT system, SMART Testing, which employs large language models to hypothesize relevant and likely failures.
  arXiv  Detail & Related papers  (2024-10-31T15:06:16Z)
• How Hard is this Test Set? NLI Characterization by Exploiting Training Dynamics [49.9329723199239]
  We propose a method for the automated creation of a challenging test set without relying on the manual construction of artificial and unrealistic examples. We categorize the test set of popular NLI datasets into three difficulty levels by leveraging methods that exploit training dynamics. When our characterization method is applied to the training set, models trained with only a fraction of the data achieve comparable performance to those trained on the full dataset.
  arXiv  Detail & Related papers  (2024-10-04T13:39:21Z)
• SYNTHEVAL: Hybrid Behavioral Testing of NLP Models with Synthetic CheckLists [59.08999823652293]
  We propose SYNTHEVAL to generate a wide range of test types for a comprehensive evaluation of NLP models. In the last stage, human experts investigate the challenging examples, manually design templates, and identify the types of failures the taskspecific models consistently exhibit. We apply SYNTHEVAL to two classification tasks, sentiment analysis and toxic language detection, and show that our framework is effective in identifying weaknesses of strong models on these tasks.
  arXiv  Detail & Related papers  (2024-08-30T17:41:30Z)
• Scalable Similarity-Aware Test Suite Minimization with Reinforcement Learning [6.9290255098776425]
  TripRL is a novel technique to produce a diverse reduced test suite with high test effectiveness.<n>We show that TripRL's runtime scales linearly with the magnitude of the Multi-Criteria Test Suite Minimization problem.
  arXiv  Detail & Related papers  (2024-08-24T08:43:03Z)
• Test Generation Strategies for Building Failure Models and Explaining Spurious Failures [4.995172162560306]
  Test inputs fail not only when the system under test is faulty but also when the inputs are invalid or unrealistic. We propose to build failure models for inferring interpretable rules on test inputs that cause spurious failures. We show that our proposed surrogate-assisted approach generates failure models with an average accuracy of 83%.
  arXiv  Detail & Related papers  (2023-12-09T18:36:15Z)
• Deep anytime-valid hypothesis testing [29.273915933729057]
  We propose a general framework for constructing powerful, sequential hypothesis tests for nonparametric testing problems. We develop a principled approach of leveraging the representation capability of machine learning models within the testing-by-betting framework. Empirical results on synthetic and real-world datasets demonstrate that tests instantiated using our general framework are competitive against specialized baselines.
  arXiv  Detail & Related papers  (2023-10-30T09:46:19Z)
• Single-Stage Visual Relationship Learning using Conditional Queries [60.90880759475021]
  TraCQ is a new formulation for scene graph generation that avoids the multi-task learning problem and the entity pair distribution. We employ a DETR-based encoder-decoder conditional queries to significantly reduce the entity label space as well. Experimental results show that TraCQ not only outperforms existing single-stage scene graph generation methods, it also beats many state-of-the-art two-stage methods on the Visual Genome dataset.
  arXiv  Detail & Related papers  (2023-06-09T06:02:01Z)
• Pre-trained Embeddings for Entity Resolution: An Experimental Analysis [Experiment, Analysis & Benchmark] [65.11858854040544]
  We perform a thorough experimental analysis of 12 popular language models over 17 established benchmark datasets. First, we assess their vectorization overhead for converting all input entities into dense embeddings vectors. Second, we investigate their blocking performance, performing a detailed scalability analysis, and comparing them with the state-of-the-art deep learning-based blocking method. Third, we conclude with their relative performance for both supervised and unsupervised matching.
  arXiv  Detail & Related papers  (2023-04-24T08:53:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.