Imagen de Portada

Identifying Flaky Tests in Quantum Code: A Machine Learning Approach

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Kaur, Khushdeep, Kim, Dongchan, Jamshidi, Ainaz, Zhang, Lei
Formato:	Preprint
Publicado:	2025
Materias:	Software Engineering Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Let Students Take the Wheel: Introducing Post-Quantum Cryptography with Active Learning
por: Jamshidi, Ainaz, et al.
Publicado: (2024)

Detecting Flaky Tests in Quantum Software: A Dynamic Approach
por: Kim, Dongchan, et al.
Publicado: (2025)

Automating Detection and Root-Cause Analysis of Flaky Tests in Quantum Software
por: Sivaloganathan, Janakan, et al.
Publicado: (2026)

Automating Quantum Software Maintenance: Flakiness Detection and Root Cause Analysis
por: Sivaloganathan, Janakan, et al.
Publicado: (2024)

FlakyFix: Using Large Language Models for Predicting Flaky Test Fix Categories and Test Code Repair
por: Fatima, Sakina, et al.
Publicado: (2023)

FlakyGuard: Automatically Fixing Flaky Tests at Industry Scale
por: Li, Chengpeng, et al.
Publicado: (2025)

A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression
por: Jamshidi, Ainaz, et al.
Publicado: (2025)

A Machine Learning-Based Error Mitigation Approach For Reliable Software Development On IBM'S Quantum Computers
por: Muqeet, Asmar, et al.
Publicado: (2024)

Automated Trustworthiness Testing for Machine Learning Classifiers
por: Cho, Steven, et al.
Publicado: (2024)

A Large Language Model Approach to Identify Flakiness in C++ Projects
por: Sun, Xin, et al.
Publicado: (2024)

Leveraging Code Cohesion Analysis to Identify Source Code Supply Chain Attacks
por: Reuben, Maor, et al.
Publicado: (2025)

Systemic Flakiness: An Empirical Analysis of Co-Occurring Flaky Test Failures
por: Parry, Owain, et al.
Publicado: (2025)

The Impact of Software Testing with Quantum Optimization Meets Machine Learning
por: Bandarupalli, Gopichand
Publicado: (2025)

Whitespaces Don't Lie: Feature-Driven and Embedding-Based Approaches for Detecting Machine-Generated Code
por: Nirob, Syed Mehedi Hasan, et al.
Publicado: (2026)

A Generic Approach to Fix Test Flakiness in Real-World Projects
por: Chen, Yang, et al.
Publicado: (2024)

The Effects of Computational Resources on Flaky Tests
por: Silva, Denini, et al.
Publicado: (2023)

A Dataset of Reproducible Flaky-Test Failures
por: Rafi, Suzzana, et al.
Publicado: (2026)

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

CA2: Code-Aware Agent for Automated Game Testing
por: Adaikkappan, Valliappan Chidambaram, et al.
Publicado: (2026)

Model Cascading for Code: A Cascaded Black-Box Multi-Model Framework for Cost-Efficient Code Completion with Self-Testing
por: Chen, Boyuan, et al.
Publicado: (2024)

Quantum vs. Classical Machine Learning Algorithms for Software Defect Prediction: Challenges and Opportunities
por: Nadim, Md, et al.
Publicado: (2024)

Targeted Test Selection Approach in Continuous Integration
por: Plyusnin, Pavel, et al.
Publicado: (2025)

Can We Classify Flaky Tests Using Only Test Code? An LLM-Based Empirical Study
por: Berndt, Alexander, et al.
Publicado: (2026)

On the Usage of Continual Learning for Out-of-Distribution Generalization in Pre-trained Language Models of Code
por: Weyssow, Martin, et al.
Publicado: (2023)

Productively Deploying Emerging Models on Emerging Platforms: A Top-Down Approach for Testing and Debugging
por: Feng, Siyuan, et al.
Publicado: (2024)

Pre-Training Representations of Binary Code Using Contrastive Learning
por: Zhang, Yifan, et al.
Publicado: (2022)

The Vocabulary of Flaky Tests in the Context of SAP HANA
por: Berndt, Alexander, et al.
Publicado: (2026)

Automated Test Validators for Flaky Cyber-Physical System Simulators: Approach and Evaluation
por: Jodat, Baharin A., et al.
Publicado: (2025)

SemRep: Generative Code Representation Learning with Code Transformations
por: Li, Weichen, et al.
Publicado: (2026)

Hardness, Structural Knowledge, and Opportunity: An Analytical Framework for Modular Performance Modeling
por: Gheibi, Omid, et al.
Publicado: (2025)

Code-Aware Prompting: A study of Coverage Guided Test Generation in Regression Setting using LLM
por: Ryan, Gabriel, et al.
Publicado: (2024)

Machine Learning Systems are Bloated and Vulnerable
por: Zhang, Huaifeng, et al.
Publicado: (2022)

Neural Variable Name Repair: Learning to Rename Identifiers for Readability
por: Yousuf, Muhammad, et al.
Publicado: (2025)

MetaSel: A Test Selection Approach for Fine-tuned DNN Models
por: Abbasishahkoo, Amin, et al.
Publicado: (2025)

CodeSAM: Source Code Representation Learning by Infusing Self-Attention with Multi-Code-View Graphs
por: Mathai, Alex, et al.
Publicado: (2024)

A Metamorphic Testing Perspective on Knowledge Distillation for Language Models of Code: Does the Student Deeply Mimic the Teacher?
por: Awal, Md. Abdul, et al.
Publicado: (2025)

Machine Learning Operations: A Mapping Study
por: Chakraborty, Abhijit, et al.
Publicado: (2024)

Robust Learning of Diverse Code Edits
por: Aggarwal, Tushar, et al.
Publicado: (2025)

Enabling Global, Human-Centered Explanations for LLMs:From Tokens to Interpretable Code and Test Generation
por: Khati, Dipin, et al.
Publicado: (2025)

AlphaTrans: A Neuro-Symbolic Compositional Approach for Repository-Level Code Translation and Validation
por: Ibrahimzada, Ali Reza, et al.
Publicado: (2024)