The paper investigates how software engineering researchers approach machine learning in their work, reviewing research, review, and education practices. It finds that while many researchers follow data collection, model training, and evaluation routines, only a minority adopt recommended practices such as hyperparameter tuning. Common challenges include data handling, evaluating non‑functional properties, and integrating human expertise, and education often relies on hands‑on activities alongside traditional methods.
By Anamaria Mojica-Hanke, David Nader Palacio, Denys Poshyvanyk, Mario Linares-V\'asquez, Steffen Herbold
The paper introduces a multi‑agent framework that transforms natural‑language MLOps tasks into verified repositories and operational cloud deployments. It uses a stateful Graph Orchestrator to coordinate agents for repository generation, review, execution, verification, release, and monitoring, ensuring lifecycle transitions only occur when supported by verifiable evidence. The framework, implemented on Google Cloud Platform, demonstrates prevention of unsupported transitions and drives each run toward a verified deployment or an auditable failure.
By Sagar Srinivas Sakhinana, Venkataramana Runkana
arXiv:2606. 17915v1 Announce Type: cross Abstract: Big-Data-as-a-Service (BDaaS) platforms require re liable automation across data ingestion, cleaning, feature engi neering, model development, deployment, and post-deployment monitoring.
By Aueaphum Aueawatthanaphisut, Badri Raj Lamichhane
arXiv:2602.24055v5 Announce Type: replace
Abstract: This study proposes CIRCLE, a six-stage, lifecycle-based framework to bridge the reality gap between model-centric performance metrics and AI syste...
By Reva Schwartz, Carina Westling, Morgan Briggs, Marzieh Fadaee, Isar Nejadgholi, Matthew Holmes, Fariza Rashid, Maya Carlyle, Afaf Ta\"ik, Kyra Wilson, Peter Douglas, Theodora Skeadas, Gabriella Waters, Rumman Chowdhury, Thiago Lacerda
arXiv:2604. 10311v2 Announce Type: replace Abstract: Artificial Intelligence (AI) models, encompassing both traditional machine learning (ML) and more advanced approaches such as deep learning and large language models (LLMs), play a central role in modern applications.
By Fabio Porto, Eduardo Ogasawara, Gabriela Moraes Botaro, Julia Neumann Bastos, Augusto Fonseca, Esther Pacitti, Patrick Valduriez
The paper reviews Trustworthy Data and Machine Learning Operations (DataOps and MLOps) for Intelligent Transportation Systems and Logistics (ITS&L). It identifies gaps in current literature, discusses the complexities, key components, tools, practical insights, and case studies relevant to ITS&L, and examines methods to strengthen trustworthiness in AI applications. The authors conclude by outlining ongoing challenges and future prospects, positioning the work as a resource for researchers, industry practitioners, and policymakers.
By Antonio Emanuele Cin\`a, Giovanni Scodeller, Cecilia Caterina Pasquale, Silvia Siri, Davide Anguita, Fabio Roli, Simona Sacone, Luca Oneto
arXiv:2606. 16842v1 Announce Type: cross Abstract: Teaching Software Engineering for AI-enabled systems entails addressing the integration of AI components within full-scale software architectures under realistic constraints.
By Amir Mashmool, Kishan Ravindra Sawant, Mojtaba Shahin, Nico Hochgeschwender, Rainer Koschke
arXiv:2609.37000v1 Announce Type: cross
Abstract: Cross-organizational collaboration is widely regarded as a key promise of SysML-based Model-Based Systems Engineering (MBSE), yet practitioners still...
By Zirui Li, Torsten Brix, Stephan Husung
arXiv:2512. 16455v4 Announce Type: replace-cross Abstract: The rapid growth of Artificial Intelligence and Machine Learning in scientific research has highlighted a gap between industry-standard MLOps tools and platforms, and the unique requirements of modern and Open Science, particularly regarding the FAIR (Findable, Accessible, Interoperable, and Reusable) principles.
By Ignacio Heredia, \'Alvaro L\'opez Garc\'ia, Fernando Aguilar G\'omez, Diego Aguirre, Caterina Alarc\'on Mar\'in, Khadijeh Alibabaei, Lisana Berberi, Miguel Caballer, Amanda Calatrava, Pedro Castro, Alessandro Costantini, Mario David, Jaime D\'iez Stefan Dlugolinsky, Borja Esteban Sanchis, Giacinto Donvito, Leonhard Duda, Sa\'ul Fernandez, Andr\'es Heredia Canales, Valentin Kozlov, Sergio Langarita, Jo\~ao Machado, Germ\'an Molt\'o, Daniel San Mart\'in, Martin \v{S}eleng, Giang Nguyen, Marcin P{\l}\'ociennik, Marta Obreg\'on Ruiz, Susana Rebolledo Ruiz, Vicente Rodriguez, Judith S\'ainz-Pardo D\'iaz, Viet Tran
arXiv:2607. 17242v1 Announce Type: cross Abstract: Pretrained machine learning (ML) models help developers build ML-intensive software systems without training models from scratch.
By Md Erfan, Ahmed Ryan, Md Rayhanur Rahman
arXiv:2607. 02731v1 Announce Type: cross Abstract: Machine learning has demonstrated significant potential for real-time monitoring, optimization, and control of scientific facilities.
By Armen Kasparian, Kishansingh Rajput, Malachi Schram, John Vennekate
arXiv:2607. 07184v1 Announce Type: cross Abstract: Pre-deployment safety evaluations aim to inform the downstream risks of releasing a new AI model.
By Marcus Williams, Hannah Sheahan, Cameron Raymond, Tomek Korbak, Deng Pan, Peilin Yang, Leon Maksin, Ningyi Xie, Phillip Guo, Ian Kivlichan, Micah Carroll