Cover Image

Quality Model for Machine Learning Components

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Lewis, Grace A., Brower-Sinning, Rachel, Edman, Robert, Ozkaya, Ipek, Echeverría, Sebastián, Derr, Alex, Beaudoin, Collin, Maffey, Katherine R.
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

Using Quality Attribute Scenarios for ML Model Test Case Generation
by: Brower-Sinning, Rachel, et al.
Published: (2024)

Defining a Reference Architecture for Edge Systems in Highly-Uncertain Environments
by: Pitstick, Kevin, et al.
Published: (2024)

A Synthesis of Green Architectural Tactics for ML-Enabled Systems
by: Järvenpää, Heli, et al.
Published: (2023)

Manifesto from Dagstuhl Perspectives Workshop 24452 -- Reframing Technical Debt
by: Avgeriou, Paris, et al.
Published: (2025)

The Product Beyond the Model -- An Empirical Study of Repositories of Open-Source ML Products
by: Nahar, Nadia, et al.
Published: (2023)

Towards Automated Detection of Inline Code Comment Smells
by: Oztas, Ipek, et al.
Published: (2025)

Semantic Neighborhood Density and Eye Gaze Time in Human Programmer Attention
by: Wallace, Robert, et al.
Published: (2026)

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

APIDA-Chat: Structured Synthesis of API Search Dialogues to Bootstrap Conversational Agents
by: Eberhart, Zachary, et al.
Published: (2025)

Human Attention During Localization of Memory Bugs in C Programs
by: Smith, Emory, et al.
Published: (2025)

CMind: An AI Agent for Localizing C Memory Bugs
by: Su, Chia-Yi, et al.
Published: (2026)

Do Code LLMs Do Static Analysis?
by: Su, Chia-Yi, et al.
Published: (2025)

Do Machines and Humans Focus on Similar Code? Exploring Explainability of Large Language Models in Code Summarization
by: Li, Jiliang, et al.
Published: (2024)

Sustainability of Machine Learning-Enabled Systems: The Machine Learning Practitioner's Perspective
by: De Martino, Vincenzo, et al.
Published: (2025)

TShape: Rescuing Machine Learning Models from Complex Shapelet Anomalies
by: Cui, Hang, et al.
Published: (2025)

Ten Essential Guidelines for Building High-Quality Research Software
by: Eisty, Nasir U., et al.
Published: (2025)

Variability-Aware Machine Learning Model Selection: Feature Modeling, Instantiation, and Experimental Case Study
by: Tavares, Cristina, et al.
Published: (2024)

Tail-aware N-version Machine Learning Models for Reliable API Recommendation
by: Matsuda, Aoi, et al.
Published: (2026)

Detecting Refactoring Commits in Machine Learning Python Projects: A Machine Learning-Based Approach
by: Noei, Shayan, et al.
Published: (2024)

From Human to Machine Refactoring: Assessing GPT-4's Impact on Python Class Quality and Readability
by: Midolo, Alessandro, et al.
Published: (2026)

Improving Deep Learning Library Testing with Machine Learning
by: Molina, Facundo, et al.
Published: (2026)

Machine Learning Evaluation Metric Discrepancies across Programming Languages and Their Components: Need for Standardization
by: Salmanpour, Mohammad R., et al.
Published: (2024)

Leveraging LLMs for the Quality Assurance of Software Requirements
by: Lubos, Sebastian, et al.
Published: (2024)

Studying the Impact of TensorFlow and PyTorch Bindings on Machine Learning Software Quality
by: Li, Hao, et al.
Published: (2024)

Maturity Framework for Enhancing Machine Learning Quality
by: Castelli, Angelantonio, et al.
Published: (2025)

Quality Issues in Machine Learning Software Systems
by: Côté, Pierre-Olivier, et al.
Published: (2023)

Modeling CubeSat Storage Battery Discharge: Equivalent Circuit Versus Machine Learning Approaches
by: Turkin, Igor, et al.
Published: (2025)

MANILA: A Low-Code Application to Benchmark Machine Learning Models and Fairness-Enhancing Methods
by: d'Aloisio, Giordano
Published: (2025)

Programmer Visual Attention During Context-Aware Code Summarization
by: Wallace, Robert, et al.
Published: (2024)

Automated Configuration Synthesis for Machine Learning Models: A git-Based Requirement and Architecture Management System
by: AlShriaf, Abdullatif, et al.
Published: (2024)

Utilizing Source Code Syntax Patterns to Detect Bug Inducing Commits using Machine Learning Models
by: Nadim, Md, et al.
Published: (2022)

A Reference Model for Information Quality in an IT Governance Context
by: Steuperaert, Dirk, et al.
Published: (2024)

Component Matching as a Graph Matching Problem
by: Kamath, Suresh
Published: (2024)

An Audit of Machine Learning Experiments on Software Defect Prediction
by: Destefanis, Giuseppe, et al.
Published: (2026)

Empirical Characterization of Logging Smells in Machine Learning Code
by: Foalem, Patrick Loic, et al.
Published: (2026)

Microservice Architecture Patterns for Scalable Machine Learning Systems
by: Karanam, Sowjanya, et al.
Published: (2026)

Understanding Feedback Mechanisms in Machine Learning Jupyter Notebooks
by: Shome, Arumoy, et al.
Published: (2024)

Empirical Characterization of Logging Smells in Machine Learning Code
by: Foalem, Patrick Loic, et al.
Published: (2026)

An Empirical Study on the Classification of Bug Reports with Machine Learning
by: Andrade, Renato, et al.
Published: (2025)

Automated Test Suite Enhancement Using Large Language Models with Few-shot Prompting
by: Chudic, Alex, et al.
Published: (2026)