Cover Image

Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Mu, Yanzhou, Wang, Rong, Zhai, Juan, Fang, Chunrong, Chen, Xiang, Wu, Jiacong, Guo, An, Shen, Jiawei, Li, Bingzhuo, Chen, Zhenyu
Format:	Preprint
Published:	2025
Subjects:	Software Engineering
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Deep Learning Framework Testing via Heuristic Guidance Based on Multiple Model Measurements
by: Zou, Yinglong, et al.
Published: (2025)

An Empirical Study of Bugs in Modern LLM Agent Frameworks
by: Zhu, Xinxue, et al.
Published: (2026)

Improving Deep Learning Framework Testing with Model-Level Metamorphic Testing
by: Mu, Yanzhou, et al.
Published: (2025)

Deep Learning Framework Testing via Model Mutation: How Far Are We?
by: Mu, Yanzhou, et al.
Published: (2025)

DevMuT: Testing Deep Learning Framework via Developer Expertise-Based Mutation
by: Mu, Yanzhou, et al.
Published: (2025)

Scalpel: Automotive Deep Learning Framework Testing via Assembling Model Components
by: Zou, Yinglong, et al.
Published: (2025)

Mutation-Based Deep Learning Framework Testing Method in JavaScript Environment
by: Zou, Yinglong, et al.
Published: (2024)

GPU Temperature Simulation-Based Testing for In-Vehicle Deep Learning Frameworks
by: Zou, Yinglong, et al.
Published: (2025)

May the Feedback Be with You! Unlocking the Power of Feedback-Driven Deep Learning Framework Fuzzing via LLMs
by: Yang, Shaoyu, et al.
Published: (2025)

Generate Realistic Test Scenes for V2X Communication Systems
by: Guo, An, et al.
Published: (2025)

Test Script Intention Generation for Mobile Application via GUI Image and Code Understanding
by: Yu, Shengcheng, et al.
Published: (2021)

ATTest: Agent-Driven Tensor Testing for Deep Learning Library Modules
by: Zhan, Zhengyu, et al.
Published: (2026)

Towards Automated Crowdsourced Testing via Personified-LLM
by: Yu, Shengcheng, et al.
Published: (2026)

Train in Vain: Functionality-Preserving Poisoning to Prevent Unauthorized Use of Code Datasets
by: Xiao, Yuan, et al.
Published: (2026)

A Large-scale Empirical Study on Fine-tuning Large Language Models for Unit Testing
by: Shang, Ye, et al.
Published: (2024)

SGAgent: Suggestion-Guided LLM-Based Multi-Agent Framework for Repository-Level Software Repair
by: Zhang, Quanjun, et al.
Published: (2026)

An Empirical Study on Challenges for LLM Application Developers
by: Chen, Xiang, et al.
Published: (2024)

A Critical Review of Large Language Model on Software Engineering: An Example from ChatGPT and Automated Program Repair
by: Zhang, Quanjun, et al.
Published: (2023)

ACTesting: Automated Cross-modal Testing Method of Text-to-Image Software
by: Gu, Siqi, et al.
Published: (2023)

RUM: Rule+LLM-Based Comprehensive Assessment on Testing Skills
by: Wang, Yue, et al.
Published: (2025)

When Autonomous Vehicle Meets V2X Cooperative Perception: How Far Are We?
by: Guo, An, et al.
Published: (2025)

Peeling Off the Cocoon: Unveiling Suppressed Golden Seeds for Mutational Greybox Fuzzing
by: Qian, Ruixiang, et al.
Published: (2026)

Probing Privacy Leaks in LLM-based Code Generation via Test Generation
by: Ge, Yifei, et al.
Published: (2026)

Scenario-Guided LLM-based Mobile App GUI Testing
by: Yu, Shengcheng, et al.
Published: (2025)

TransformCode: A Contrastive Learning Framework for Code Embedding via Subtree Transformation
by: Xian, Zixiang, et al.
Published: (2023)

An Empirical Study on Noisy Label Learning for Program Understanding
by: Wang, Wenhan, et al.
Published: (2023)

Log-based, Business-aware REST API Testing
by: Yang, Ding, et al.
Published: (2026)

EvoRepair: Enhancing Vulnerability Repair Agents Through Experience-Based Self-Evolution
by: Hu, Haichuan, et al.
Published: (2026)

On the Effectiveness of Code Representation in Deep Learning-Based Automated Patch Correctness Assessment
by: Zhang, Quanjun, et al.
Published: (2026)

TestART: Improving LLM-based Unit Testing via Co-evolution of Automated Generation and Repair Iteration
by: Gu, Siqi, et al.
Published: (2024)

A Systematic Literature Review on Large Language Models for Automated Program Repair
by: Zhang, Quanjun, et al.
Published: (2024)

Enhancing and Reporting Robustness Boundary of Neural Code Models for Intelligent Code Understanding
by: Han, Tingxu, et al.
Published: (2026)

ComPass: Contrastive Learning for Automated Patch Correctness Assessment in Program Repair
by: Zhang, Quanjun, et al.
Published: (2026)

Effective, Platform-Independent GUI Testing via Image Embedding and Reinforcement Learning
by: Yu, Shengcheng, et al.
Published: (2022)

Breaking, Stale, or Missing? Benchmarking Coding Agents on Project-Level Test Evolution
by: Shang, Ye, et al.
Published: (2026)

Practical, Automated Scenario-based Mobile App Testing
by: Yu, Shengcheng, et al.
Published: (2024)

Large Language Models for Unit Testing: A Systematic Literature Review
by: Zhang, Quanjun, et al.
Published: (2025)

No Man is an Island: Towards Fully Automatic Programming by Code Search, Code Generation and Program Repair
by: Zhang, Quanjun, et al.
Published: (2024)

TestBench: Evaluating Class-Level Test Case Generation Capability of Large Language Models
by: Zhang, Quanjun, et al.
Published: (2024)

Toward Understanding Deep Learning Framework Bugs
by: Chen, Junjie, et al.
Published: (2022)