Image de couverture de livre

The Impact of Software Testing with Quantum Optimization Meets Machine Learning

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteur principal:	Bandarupalli, Gopichand
Format:	Preprint
Publié:	2025
Sujets:	Software Engineering Artificial Intelligence Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

AI-Driven Code Refactoring: Using Graph Neural Networks to Enhance Software Maintainability
par: Bandarupalli, Gopichand
Publié: (2025)

Code Reborn AI-Driven Legacy Systems Modernization from COBOL to Java
par: Bandarupalli, Gopichand
Publié: (2025)

ML-On-Rails: Safeguarding Machine Learning Models in Software Systems A Case Study
par: Abdelkader, Hala, et autres
Publié: (2024)

LLMs in Coding and their Impact on the Commercial Software Engineering Landscape
par: Belozerov, Vladislav, et autres
Publié: (2025)

SWE-Replay: Efficient Test-Time Scaling for Software Engineering Agents
par: Ding, Yifeng, et autres
Publié: (2026)

Sustainable Machine Learning Retraining: Optimizing Energy Efficiency Without Compromising Accuracy
par: Poenaru-Olaru, Lorena, et autres
Publié: (2025)

On the Replicability and Reproducibility of Deep Learning in Software Engineering
par: Liu, Chao, et autres
Publié: (2020)

Can Search-Based Testing with Pareto Optimization Effectively Cover Failure-Revealing Test Inputs?
par: Sorokin, Lev, et autres
Publié: (2024)

StepShield: When, Not Whether to Intervene on Rogue Agents
par: Felicia, Gloria, et autres
Publié: (2026)

Utilizing Deep Learning to Optimize Software Development Processes
par: Li, Keqin, et autres
Publié: (2024)

VibeTensor: System Software for Deep Learning, Fully Generated by AI Agents
par: Xu, Bing, et autres
Publié: (2026)

Machine Learning Robustness: A Primer
par: Braiek, Houssem Ben, et autres
Publié: (2024)

Machine Learning with Requirements: a Manifesto
par: Giunchiglia, Eleonora, et autres
Publié: (2023)

SWE-MiniSandbox: Container-Free Reinforcement Learning for Building Software Engineering Agents
par: Yuan, Danlong, et autres
Publié: (2026)

DeepKnowledge: Generalisation-Driven Deep Learning Testing
par: Missaoui, Sondess, et autres
Publié: (2024)

Is ChatGPT a Good Software Librarian? An Exploratory Study on the Use of ChatGPT for Software Library Recommendations
par: Latendresse, Jasmine, et autres
Publié: (2024)

Machine Learning Models for the Early Detection of Burnout in Software Engineering: a Systematic Literature Review
par: Tulili, Tien Rahayu, et autres
Publié: (2026)

Challenges and Paths Towards AI for Software Engineering
par: Gu, Alex, et autres
Publié: (2025)

ChatGPT Incorrectness Detection in Software Reviews
par: Tanzil, Minaoar Hossain, et autres
Publié: (2024)

MIST-RL: Mutation-based Incremental Suite Testing via Reinforcement Learning
par: Zhu, Sicheng, et autres
Publié: (2026)

asanAI: In-Browser, No-Code, Offline-First Machine Learning Toolkit
par: Koch, Norman, et autres
Publié: (2025)

Adaptive Detection of Software Aging under Workload Shift
par: Silva, Rafael Jose Moura, et autres
Publié: (2025)

Model Lake: a New Alternative for Machine Learning Models Management and Governance
par: Garouani, Moncef, et autres
Publié: (2025)

Machine Learning Systems: A Survey from a Data-Oriented Perspective
par: Cabrera, Christian, et autres
Publié: (2023)

The Role of Artificial Intelligence and Machine Learning in Software Testing
par: Ramadan, Ahmed, et autres
Publié: (2024)

Toward Explaining Large Language Models in Software Engineering Tasks
par: Vitale, Antonio, et autres
Publié: (2025)

EGSS: Entropy-guided Stepwise Scaling for Reliable Software Engineering
par: Mao, Chenhui, et autres
Publié: (2026)

DRAGON: Robust Classification for Very Large Collections of Software Repositories
par: Balla, Stefano, et autres
Publié: (2026)

Towards Transparent and Accurate Diabetes Prediction Using Machine Learning and Explainable Artificial Intelligence
par: Khokhar, Pir Bakhsh, et autres
Publié: (2025)

Function+Data Flow: A Framework to Specify Machine Learning Pipelines for Digital Twinning
par: de Conto, Eduardo, et autres
Publié: (2024)

Automated Machine Learning: A Case Study on Non-Intrusive Appliance Load Monitoring
par: Moin, Armin, et autres
Publié: (2022)

Supersonic: Learning to Generate Source Code Optimizations in C/C++
par: Chen, Zimin, et autres
Publié: (2023)

Foundation Model Engineering: Engineering Foundation Models Just as Engineering Software
par: Ran, Dezhi, et autres
Publié: (2024)

SetupBench: Assessing Software Engineering Agents' Ability to Bootstrap Development Environments
par: Arora, Avi, et autres
Publié: (2025)

The Hitchhikers Guide to Production-ready Trustworthy Foundation Model powered Software (FMware)
par: Vasilevski, Kirill, et autres
Publié: (2025)

Protocol-Driven Development: Governing Generated Software Through Invariants and Continuous Evidence
par: He, Jun, et autres
Publié: (2026)

Towards a Classification of Open-Source ML Models and Datasets for Software Engineering
par: González, Alexandra, et autres
Publié: (2024)

A Large-Scale Study of Model Integration in ML-Enabled Software Systems
par: Sens, Yorick, et autres
Publié: (2024)

GSO: Challenging Software Optimization Tasks for Evaluating SWE-Agents
par: Shetty, Manish, et autres
Publié: (2025)

Explainable Artificial Intelligence Techniques for Software Development Lifecycle: A Phase-specific Survey
par: Arora, Lakshit, et autres
Publié: (2025)