Copertina

DevMuT: Testing Deep Learning Framework via Developer Expertise-Based Mutation

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Mu, Yanzhou, Zhai, Juan, Fang, Chunrong, Chen, Xiang, Cao, Zhixiang, Yang, Peiran, Zou, Yinglong, Zheng, Tao, Chen, Zhenyu
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Software Engineering
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

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

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

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

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

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

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

Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis
di: Mu, Yanzhou, et al.
Pubblicazione: (2025)

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

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

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

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

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

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

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

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

MuMuTestUp: Mutation-based Multi-Agent Test Case Update
di: Tian, Dawei, et al.
Pubblicazione: (2026)

MuFF: Stable and Sensitive Post-training Mutation Testing for Deep Learning
di: Kim, Jinhan, et al.
Pubblicazione: (2025)

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

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

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

MuSe: a Mutation Testing Plugin for the Remix IDE
di: Iuliano, Gerardo, et al.
Pubblicazione: (2026)

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

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

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

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

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

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

Vision-Based Mobile App GUI Testing: A Survey
di: Yu, Shengcheng, et al.
Pubblicazione: (2023)

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

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

CooTest: An Automated Testing Approach for V2X Communication Systems
di: Guo, An, et al.
Pubblicazione: (2024)

Improving Retrieval-Augmented Deep Assertion Generation via Joint Training
di: Zhang, Quanjun, et al.
Pubblicazione: (2025)

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

Improving Deep Assertion Generation via Fine-Tuning Retrieval-Augmented Pre-trained Language Models
di: Zhang, Quanjun, et al.
Pubblicazione: (2025)

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

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

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

Coverage Goal Selector for Combining Multiple Criteria in Search-Based Unit Test Generation
di: Zhou, Zhichao, et al.
Pubblicazione: (2023)

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

DevGPT: Studying Developer-ChatGPT Conversations
di: Xiao, Tao, et al.
Pubblicazione: (2023)