Cover Image

QuanForge: A Mutation Testing Framework for Quantum Neural Networks

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Shao, Minqi, Xia, Shangzhou, Zhao, Jianjun
Format:	Preprint
Published:	2026
Subjects:	Software Engineering Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

QuanTest: Entanglement-Guided Testing of Quantum Neural Network Systems
by: Shi, Jinjing, et al.
Published: (2024)

NovaQ: Improving Quantum Program Testing through Diversity-Guided Test Case Generation
by: Jin, Tiancheng, et al.
Published: (2025)

QEMI: A Quantum Software Stacks Testing Framework via Equivalence Modulo Inputs
by: Luo, Junjie, et al.
Published: (2026)

Quantum Concolic Testing
by: Xia, Shangzhou, et al.
Published: (2024)

QSolver: A Quantum Constraint Solver
by: Xia, Shangzhou, et al.
Published: (2026)

Is Measurement Enough? Rethinking Output Validation in Quantum Program Testing
by: Ye, Jiaming, et al.
Published: (2025)

QuanBench: Benchmarking Quantum Code Generation with Large Language Models
by: Guo, Xiaoyu, et al.
Published: (2025)

QuanUML: Towards A Modeling Language for Model-Driven Quantum Software Development
by: Guo, Xiaoyu, et al.
Published: (2025)

MutaBot: A Mutation Testing Approach for Chatbots
by: Urrico, Michael Ferdinando, et al.
Published: (2024)

QSPE: Enumerating Skeletal Quantum Programs for Quantum Library Testing
by: Ye, Jiaming, et al.
Published: (2026)

AgentForge: Execution-Grounded Multi-Agent LLM Framework for Autonomous Software Engineering
by: Kumar, Rajesh, et al.
Published: (2026)

Rethinking Diversity in Deep Neural Network Testing
by: Wang, Zi, et al.
Published: (2023)

Mutation-based Consistency Testing for Evaluating the Code Understanding Capability of LLMs
by: Li, Ziyu, et al.
Published: (2024)

QuanBench+: A Unified Multi-Framework Benchmark for LLM-Based Quantum Code Generation
by: Slim, Ali, et al.
Published: (2026)

Re-Evaluating Code LLM Benchmarks Under Semantic Mutation
by: Pan, Zhiyuan, et al.
Published: (2025)

AppForge: From Assistant to Independent Developer -- Are GPTs Ready for Software Development?
by: Ran, Dezhi, et al.
Published: (2025)

GenCode: A Generic Data Augmentation Framework for Boosting Deep Learning-Based Code Understanding
by: Dong, Zeming, et al.
Published: (2024)

HFuzzer: Testing Large Language Models for Package Hallucinations via Phrase-based Fuzzing
by: Zhao, Yukai, et al.
Published: (2025)

Mutation-Guided LLM-based Test Generation at Meta
by: Foster, Christopher, et al.
Published: (2025)

LLMs for Automated Unit Test Generation and Assessment in Java: The AgoneTest Framework
by: Lops, Andrea, et al.
Published: (2025)

Saving SWE-Bench: A Benchmark Mutation Approach for Realistic Agent Evaluation
by: Garg, Spandan, et al.
Published: (2025)

TREAT: A Code LLMs Trustworthiness / Reliability Evaluation and Testing Framework
by: Gao, Shuzheng, et al.
Published: (2025)

MIST-RL: Mutation-based Incremental Suite Testing via Reinforcement Learning
by: Zhu, Sicheng, et al.
Published: (2026)

FlaKat: A Machine Learning-Based Categorization Framework for Flaky Tests
by: Lin, Shizhe, et al.
Published: (2024)

Towards Reliable Evaluation of Neural Program Repair with Natural Robustness Testing
by: Le-Cong, Thanh, et al.
Published: (2024)

Faster Configuration Performance Bug Testing with Neural Dual-level Prioritization
by: Ma, Youpeng, et al.
Published: (2025)

MILE: A Mutation Testing Framework of In-Context Learning Systems
by: Wei, Zeming, et al.
Published: (2024)

RBT4DNN: Requirements-based Testing of Neural Networks
by: Mozumder, Nusrat Jahan, et al.
Published: (2025)

FuzzAug: Data Augmentation by Coverage-guided Fuzzing for Neural Test Generation
by: He, Yifeng, et al.
Published: (2024)

Are Large Language Models Robust in Understanding Code Against Semantics-Preserving Mutations?
by: Orvalho, Pedro, et al.
Published: (2025)

Trae Agent: An LLM-based Agent for Software Engineering with Test-time Scaling
by: Trae Research Team, et al.
Published: (2025)

A Methodological Analysis of Empirical Studies in Quantum Software Testing
by: Li, Yuechen, et al.
Published: (2026)

Why Attention Fails: A Taxonomy of Faults in Attention-Based Neural Networks
by: Jahan, Sigma, et al.
Published: (2025)

DeMuVGN: Effective Software Defect Prediction Model by Learning Multi-view Software Dependency via Graph Neural Networks
by: Qiao, Yu, et al.
Published: (2024)

Semantic-Aware Fuzzing: An Empirical Framework for LLM-Guided, Reasoning-Driven Input Mutation
by: Lu, Mengdi, et al.
Published: (2025)

AgenticTCAD: A LLM-based Multi-Agent Framework for Automated TCAD Code Generation and Device Optimization
by: Fan, Guangxi, et al.
Published: (2025)

A Black-box Testing Framework for Oracle Quantum Programs
by: Long, Peixun, et al.
Published: (2025)

AutoSafeCoder: A Multi-Agent Framework for Securing LLM Code Generation through Static Analysis and Fuzz Testing
by: Nunez, Ana, et al.
Published: (2024)

ACE: Self-Evolving LLM Coding Framework via Adversarial Unit Test Generation and Preference Optimization
by: Huang, Yixu, et al.
Published: (2026)

Probabilistic Verification of Neural Networks via Efficient Probabilistic Hull Generation
by: Li, Jingyang, et al.
Published: (2026)