DNN Testing

DNN: Deep Neural Network

Latest papers

11 papers1 paper in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Grounding Deep Learning Framework Testing in Data Flow
Oct 11, 2026Jiatao Ge, Yanzhou Mu, Yinglong Zou +3Automated Software Testing DNN Testing
State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China · UNIST, Ulsan, Republic of Korea
TestifAI: Tomography-Based Testing for Deep Learning Systems
Aug 19, 2026Arooj Arif, Tobias Hartung, Elena Botoeva +1DNN Testing Neural Network Robustness
Northeastern University London London, United Kingdom · University of Kent Canterbury, United Kingdom
ADEPT: A Unified Framework for Deep Learning Test Adequacy
Aug 12, 2026Yidi Kao, Shawn Burnham, Tommi Rose Fahy +1DNN Testing
Auburn University · Department of Computer Science and Software Engineering · Auburn, USA
SeFaR: Semantic Feature-aware Robustness Testing of Deep Neural Networks
Aug 10, 2026Nusrat Jahan Mozumder, Divya Gopinath, Corina Pasareanu +1Neural Network Verification Neural Network Robustness
University of Virginia · USA · KBR Inc., NASA Ames +1
Test Case Prioritization for DNNs via Neural Collapse Instability
Jul 22, 2026Chunyu Liu, Mingyuan Li, Yang Li +4Automated Software Testing DNN Testing
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, China · National Engineering Research Center of Disaster Backup and Recovery, Beijing University of Posts and Telecommunications, China · Information Security Center, Beijing University of Posts and Telecommunications, China
AdaStop: Cost-Aware Early Stopping for DNN Test Selection
Jul 6, 2026Bonan Shen, Wei-Jung Huang, Xin Liu +2Early Stopping DNN Testing
Independent Researcher
Testing Neural Networks via Bayesian-Guided Exploration of Decision Landscapes
Jun 3, 2026Bin Duan, Meiru Che, Guowei YangDNN Testing
School of Electrical Engineering and Computer Science, The University of Queensland, Australia · College of Information and Communications Technology, Central Queensland University, Australia
Latent Anchor-Driven Test Generation for Deep Neural Networks
Jun 3, 2026Bin Duan, Matthew B. Dwyer, Guowei YangDNN Testing Automated Test Generation
School of Electrical Engineering and Computer Science, The University of Queensland, Australia · Department of Computer Science, University of Virginia, United States
CAFD: Concept-Aware DNN Fault Detection using VLMs
May 19, 2026Amin Abbasishahkoo, Mahboubeh Dadkhah, Lionel BriandDNN Testing Anomaly Detection
School of EECS, University of Ottawa, Ottawa, ON K1N 6N5, Canada · Research Ireland Lero centre for software, University of Limerick, Ireland
TopoMap: A Feature-based Discriminator of the Topographical Regions in the Test Input Space
Sep 3, 2025Gianmarco De Vita, Nargiz Humbatova, Paolo TonellaDNN Testing
Università della Svizzera italiana, Lugano, Switzerland
Towards Understanding Deep Learning Model in Image Recognition via Coverage Test
May 12, 2025Wenkai Li, Xiaoqi Li, Yingjie Mao +1Neural Network Verification Convolutional Neural Networks
Hainan University, Haikou, China

Oct 11, 2026J/K move · Enter open · S save

Grounding Deep Learning Framework Testing in Data Flow

Jiatao Ge, Yanzhou Mu, Yinglong Zou, Yibo Hu+2

State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China · UNIST, Ulsan, Republic of Korea

Automated Software Testing DNN Testing Automated Test Generation Mutation Testing

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Data flow underlies deep learning (DL) framework execution, including automatic differentiation and operator-fusion optimization. Bugs involving data flow can cause incorrect results or unexpected behavior. Existing testing methods represent models primarily by operator topology, omitting runtime tensor values and properties and operator input requirements. They therefore cannot distinguish data flows in models with similar topologies, even when these differences affect framework behavior. We introduce the Tensor Flow Graph (TFG) to capture runtime tensor states and operator input requirements. Its nodes represent operators and their input requirements, while its edges record tensor values and properties. Building on the TFG, we propose FlowMuT, a mutation-based testing method grounded in data flow. Mutations are represented as constrained subgraph replacements, with mutation operators derived from prior work and an analysis of mutable data flow objects. In each round, FlowMuT builds a TFG for the seed model, pairs mutation operators with TFG sites, and filters illegal candidates using data flow constraints. It uses LinUCB to select a candidate based on coverage and impact, executes the mutant, updates selection feedback, and uses each accepted mutant as the next seed. We evaluate FlowMuT on PyTorch and MindSpore using representative models from diverse application scenarios. FlowMuT submitted 55 bug reports for cases missed by all baseline methods; developers confirmed 41 reports and fixed the bugs described in 7 reports. FlowMuT achieves 43.4% higher data flow diversity than the strongest baseline and a legal mutant rate of 96.7%, compared with 86.4% without constraints. These results show that testing from a data flow perspective is practical and effective.