| 2022 | A new approach for machine learning security risk assessment: work in progress. Jun Yajima, Maki Inui, Takanori Oikawa, Fumiyoshi Kasahara, Ikuya Morikawa, Nobukazu Yoshioka |
| 2022 | AI governance in the system development life cycle: insights on responsible machine learning engineering. Samuli Laato, Teemu Birkstedt, Matti Mäntymäki, Matti Minkkinen, Tommi Mikkonen |
| 2022 | An empirical evaluation of flow based programming in the machine learning deployment context. Andrei Paleyes, Christian Cabrera, Neil D. Lawrence |
| 2022 | Black-box models for non-functional properties of AI software systems. Birte Friesel, Olaf Spinczyk |
| 2022 | Checkpointing and deterministic training for deep learning. Xiangzhe Xu, Hongyu Liu, Guanhong Tao, Zhou Xuan, Xiangyu Zhang |
| 2022 | Code smells for machine learning applications. Haiyin Zhang, Luís Cruz, Arie van Deursen |
| 2022 | Data is about detail: an empirical investigation for software systems with NLP at core. Anmol Singhal, Preethu Rose Anish, Pratik Sonar, Smita S. Ghaisas |
| 2022 | Data smells in public datasets. Arumoy Shome, Luís Cruz, Arie van Deursen |
| 2022 | Data smells: categories, causes and consequences, and detection of suspicious data in AI-based systems. Harald Foidl, Michael Felderer, Rudolf Ramler |
| 2022 | Data sovereignty for AI pipelines: lessons learned from an industrial project at Mondragon corporation. Marcel Altendeitering, Julia Pampus, Felix Larrinaga, Jon Legaristi, Falk Howar |
| 2022 | Engineering a platform for reinforcement learning workloads. Ali Kanso, Kinshuman Patra |
| 2022 | Exploring ML testing in practice: lessons learned from an interactive rapid review with axis communications. Qunying Song, Markus Borg, Emelie Engström, Håkan Ardö, Sergio Rico |
| 2022 | Identification of out-of-distribution cases of CNN using class-based surprise adequacy. Mira Marhaba, Ettore Merlo, Foutse Khomh, Giuliano Antoniol |
| 2022 | Improving generalizability of ML-enabled software through domain specification. Hamed Barzamini, Mona Rahimi, Murtuza Shahzad, Hamed Alhoori |
| 2022 | Influence-driven data poisoning in graph-based semi-supervised classifiers. Adriano Franci, Maxime Cordy, Martin Gubri, Mike Papadakis, Yves Le Traon |
| 2022 | MLOps: five steps to guide its effective implementation. Beatriz M. A. Matsui, Denise H. Goya |
| 2022 | Method cards for prescriptive machine-learning transparency. David Adkins, Bilal Alsallakh, Adeel Cheema, Narine Kokhlikyan, Emily McReynolds, Pushkar Mishra, Chavez Procope, Jeremy Sawruk, Erin Wang, Polina Zvyagina |
| 2022 | Practical insights of repairing model problems on image classification. Akihito Yoshii, Susumu Tokumoto, Fuyuki Ishikawa |
| 2022 | Preliminary insights to enable automation of the software development process in software StartUps: an investigation study from the use of artificial intelligence and machine learning. Olimar Teixeira Borges, Valentina Lenarduzzi, Rafael Prikladnicki |
| 2022 | Proceedings of the 1st International Conference on AI Engineering: Software Engineering for AI, CAIN 2022, Pittsburgh, Pennsylvania, May 16-17, 2022 Ivica Crnkovic |
| 2022 | Pynblint: a static analyzer for Python Jupyter notebooks. Luigi Quaranta, Fabio Calefato, Filippo Lanubile |
| 2022 | Quality assurance of generative dialog models in an evolving conversational agent used for Swedish language practice. Markus Borg, Johan Bengtsson, Harald Österling, Alexander Hagelborn, Isabella Gagner, Piotr Tomaszewski |
| 2022 | Robust active learning: sample-efficient training of robust deep learning models. Yuejun Guo, Qiang Hu, Maxime Cordy, Mike Papadakis, Yves Le Traon |
| 2022 | Structural causal models as boundary objects in AI system development. Hans-Martin Heyn, Eric Knauss |
| 2022 | The goldilocks framework: towards selecting the optimal approach to conducting AI projects. Rimma Dzhusupova, Jan Bosch, Helena Holmström Olsson |
| 2022 | TopSelect: a topology-based feature selection method for industrial machine learning. Hadil Abukwaik, Lefter Sula, Pablo Rodriguez |
| 2022 | Towards a methodological framework for production-ready AI-based software components. Markus Haug, Justus Bogner |
| 2022 | Towards a roadmap on software engineering for responsible AI. Qinghua Lu, Liming Zhu, Xiwei Xu, Jon Whittle, Zhenchang Xing |
| 2022 | Traceable business-to-safety analysis framework for safety-critical machine learning systems. Jati H. Husen, Hironori Washizaki, Hnin Thandar Tun, Nobukazu Yoshioka, Yoshiaki Fukazawa, Hironori Takeuchi |
| 2022 | UDAVA: an unsupervised learning pipeline for sensor data validation in manufacturing. Erik Johannes Husom, Simeon Tverdal, Arda Goknil, Sagar Sen |
| 2022 | What is an AI engineer?: an empirical analysis of job ads in The Netherlands. Marcel Meesters, Petra Heck, Alexander Serebrenik |
| 2022 | What is software quality for AI engineers?: towards a thinning of the fog. Valentina Golendukhina, Valentina Lenarduzzi, Michael Felderer |