Saved in:
| Main Authors: | Ahmed, Zaheed, Dapaah, Emmanuel Charleson, Makedonski, Philip, Grabowski, Jens |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.22640 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
When Data Quality Issues Collide: A Large-Scale Empirical Study of Co-Occurring Data Quality Issues in Software Defect Prediction
by: Dapaah, Emmanuel Charleson, et al.
Published: (2025)
by: Dapaah, Emmanuel Charleson, et al.
Published: (2025)
An Empirical Study of the Realism of Mutants in Deep Learning
by: Ahmed, Zaheed, et al.
Published: (2025)
by: Ahmed, Zaheed, et al.
Published: (2025)
Inferring Data Preconditions from Deep Learning Models for Trustworthy Prediction in Deployment
by: Ahmed, Shibbir, et al.
Published: (2024)
by: Ahmed, Shibbir, et al.
Published: (2024)
DeepKnowledge: Generalisation-Driven Deep Learning Testing
by: Missaoui, Sondess, et al.
Published: (2024)
by: Missaoui, Sondess, et al.
Published: (2024)
On Accelerating Deep Neural Network Mutation Analysis by Neuron and Mutant Clustering
by: Lyons, Lauren, et al.
Published: (2025)
by: Lyons, Lauren, et al.
Published: (2025)
Fault Localization via Fine-tuning Large Language Models with Mutation Generated Stack Traces
by: Jambigi, Neetha, et al.
Published: (2025)
by: Jambigi, Neetha, et al.
Published: (2025)
MIST-RL: Mutation-based Incremental Suite Testing via Reinforcement Learning
by: Zhu, Sicheng, et al.
Published: (2026)
by: Zhu, Sicheng, et al.
Published: (2026)
Machine Learning Systems are Bloated and Vulnerable
by: Zhang, Huaifeng, et al.
Published: (2022)
by: Zhang, Huaifeng, et al.
Published: (2022)
Targeted Deep Learning System Boundary Testing
by: Weißl, Oliver, et al.
Published: (2024)
by: Weißl, Oliver, et al.
Published: (2024)
DeepGD: A Multi-Objective Black-Box Test Selection Approach for Deep Neural Networks
by: Aghababaeyan, Zohreh, et al.
Published: (2023)
by: Aghababaeyan, Zohreh, et al.
Published: (2023)
Toward Debugging Deep Reinforcement Learning Programs with RLExplorer
by: Bouchoucha, Rached, et al.
Published: (2024)
by: Bouchoucha, Rached, et al.
Published: (2024)
Reinforcement Learning from Automatic Feedback for High-Quality Unit Test Generation
by: Steenhoek, Benjamin, et al.
Published: (2023)
by: Steenhoek, Benjamin, et al.
Published: (2023)
Reinforcement Learning from Automatic Feedback for High-Quality Unit Test Generation
by: Steenhoek, Benjamin, et al.
Published: (2024)
by: Steenhoek, Benjamin, et al.
Published: (2024)
GIST: Generated Inputs Sets Transferability in Deep Learning
by: Tambon, Florian, et al.
Published: (2023)
by: Tambon, Florian, et al.
Published: (2023)
Deep Learning and Machine Learning: Advancing Big Data Analytics and Management with Design Patterns
by: Chen, Keyu, et al.
Published: (2024)
by: Chen, Keyu, et al.
Published: (2024)
Learning-Based Testing for Deep Learning: Enhancing Model Robustness with Adversarial Input Prioritization
by: Rahman, Sheikh Md Mushfiqur, et al.
Published: (2025)
by: Rahman, Sheikh Md Mushfiqur, et al.
Published: (2025)
Towards Enhancing the Reproducibility of Deep Learning Bugs: An Empirical Study
by: Shah, Mehil B., et al.
Published: (2024)
by: Shah, Mehil B., et al.
Published: (2024)
A Comprehensive Study of Deep Learning Model Fixing Approaches
by: You, Hanmo, et al.
Published: (2025)
by: You, Hanmo, et al.
Published: (2025)
Investigating Reproducibility in Deep Learning-Based Software Fault Prediction
by: Mukhtar, Adil, et al.
Published: (2024)
by: Mukhtar, Adil, et al.
Published: (2024)
Learning Service Selection Decision Making Behaviors During Scientific Workflow Development
by: Xie, Xihao, et al.
Published: (2024)
by: Xie, Xihao, et al.
Published: (2024)
High-Quality Tabular Data Generation using Post-Selected VAE
by: Shulakov, Volodymyr
Published: (2024)
by: Shulakov, Volodymyr
Published: (2024)
Mutation-Guided LLM-based Test Generation at Meta
by: Foster, Christopher, et al.
Published: (2025)
by: Foster, Christopher, et al.
Published: (2025)
Data Virtualization for Machine Learning
by: Khan, Saiful, et al.
Published: (2025)
by: Khan, Saiful, et al.
Published: (2025)
Bug-Report-Driven Fault Localization: Industrial Benchmarking and Lesson Learned at ABB Robotics
by: Hall, Pernilla, et al.
Published: (2026)
by: Hall, Pernilla, et al.
Published: (2026)
Good Tools are Half the Work: Tool Usage in Deep Learning Projects
by: Panourgia, Evangelia, et al.
Published: (2023)
by: Panourgia, Evangelia, et al.
Published: (2023)
RepDL: Bit-level Reproducible Deep Learning Training and Inference
by: Xie, Peichen, et al.
Published: (2025)
by: Xie, Peichen, et al.
Published: (2025)
Unified Implementations of Recurrent Neural Networks in Multiple Deep Learning Frameworks
by: Martinuzzi, Francesco
Published: (2025)
by: Martinuzzi, Francesco
Published: (2025)
GPU Temperature Simulation-Based Testing for In-Vehicle Deep Learning Frameworks
by: Zou, Yinglong, et al.
Published: (2025)
by: Zou, Yinglong, et al.
Published: (2025)
An Exploratory Study on Automatic Identification of Assumptions in the Development of Deep Learning Frameworks
by: Yang, Chen, et al.
Published: (2024)
by: Yang, Chen, et al.
Published: (2024)
Bridging Expert Knowledge with Deep Learning Techniques for Just-In-Time Defect Prediction
by: Zhou, Xin, et al.
Published: (2024)
by: Zhou, Xin, et al.
Published: (2024)
Fault Localization in Deep Learning-based Software: A System-level Approach
by: Morovati, Mohammad Mehdi, et al.
Published: (2024)
by: Morovati, Mohammad Mehdi, et al.
Published: (2024)
Learning to Parallelize with OpenMP by Augmented Heterogeneous AST Representation
by: Chen, Le, et al.
Published: (2023)
by: Chen, Le, et al.
Published: (2023)
TeleResilienceBench: Quantifying Resilience for LLM Reasoning in Telecommunications
by: Gajjar, Pranshav, et al.
Published: (2026)
by: Gajjar, Pranshav, et al.
Published: (2026)
Scalable and Precise Patch Robustness Certification for Deep Learning Models with Top-k Predictions
by: Zhou, Qilin, et al.
Published: (2025)
by: Zhou, Qilin, et al.
Published: (2025)
Improving the Reproducibility of Deep Learning Software: An Initial Investigation through a Case Study Analysis
by: Ravi, Nikita, et al.
Published: (2025)
by: Ravi, Nikita, et al.
Published: (2025)
Analysis of Failures and Risks in Deep Learning Model Converters: A Case Study in the ONNX Ecosystem
by: Jajal, Purvish, et al.
Published: (2023)
by: Jajal, Purvish, et al.
Published: (2023)
LogSieve: Task-Aware CI Log Reduction for Sustainable LLM-Based Analysis
by: Barnes, Marcus Emmanuel, et al.
Published: (2026)
by: Barnes, Marcus Emmanuel, et al.
Published: (2026)
Language Models are Better Bug Detector Through Code-Pair Classification
by: Alrashedy, Kamel, et al.
Published: (2023)
by: Alrashedy, Kamel, et al.
Published: (2023)
Maturity Framework for Enhancing Machine Learning Quality
by: Castelli, Angelantonio, et al.
Published: (2025)
by: Castelli, Angelantonio, et al.
Published: (2025)
Quality Issues in Machine Learning Software Systems
by: Côté, Pierre-Olivier, et al.
Published: (2023)
by: Côté, Pierre-Olivier, et al.
Published: (2023)
Similar Items
-
When Data Quality Issues Collide: A Large-Scale Empirical Study of Co-Occurring Data Quality Issues in Software Defect Prediction
by: Dapaah, Emmanuel Charleson, et al.
Published: (2025) -
An Empirical Study of the Realism of Mutants in Deep Learning
by: Ahmed, Zaheed, et al.
Published: (2025) -
Inferring Data Preconditions from Deep Learning Models for Trustworthy Prediction in Deployment
by: Ahmed, Shibbir, et al.
Published: (2024) -
DeepKnowledge: Generalisation-Driven Deep Learning Testing
by: Missaoui, Sondess, et al.
Published: (2024) -
On Accelerating Deep Neural Network Mutation Analysis by Neuron and Mutant Clustering
by: Lyons, Lauren, et al.
Published: (2025)