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ORCIDCAIN 2022: Pittsburgh, PA, USA
- Ivica Crnkovic:
Proceedings of the 1st International Conference on AI Engineering: Software Engineering for AI, CAIN 2022, Pittsburgh, Pennsylvania, May 16-17, 2022. ACM 2022, ISBN 978-1-4503-9275-4
Quality assurance
- Valentina Golendukhina, Valentina Lenarduzzi, Michael Felderer:
What is software quality for AI engineers?: towards a thinning of the fog. 1-9 - Qunying Song, Markus Borg, Emelie Engström, Håkan Ardö, Sergio Rico:
Exploring ML testing in practice: lessons learned from an interactive rapid review with axis communications. 10-21 - Markus Borg, Johan Bengtsson, Harald Österling, Alexander Hagelborn, Isabella Gagner, Piotr Tomaszewski:
Quality assurance of generative dialog models in an evolving conversational agent used for Swedish language practice. 22-32
Posters
- Beatriz M. A. Matsui
, Denise H. Goya:
MLOps: five steps to guide its effective implementation. 33-34 - Markus Haug, Justus Bogner:
Towards a methodological framework for production-ready AI-based software components. 35-36 - Olimar Teixeira Borges, Valentina Lenarduzzi, Rafael Prikladnicki:
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. 37-38 - Mira Marhaba, Ettore Merlo, Foutse Khomh, Giuliano Antoniol:
Identification of out-of-distribution cases of CNN using class-based surprise adequacy. 39-40 - Yuejun Guo, Qiang Hu, Maxime Cordy, Mike Papadakis, Yves Le Traon:
Robust active learning: sample-efficient training of robust deep learning models. 41-42 - Hans-Martin Heyn, Eric Knauss:
Structural causal models as boundary objects in AI system development. 43-45 - Hadil Abukwaik, Lefter Sula, Pablo Rodriguez:
TopSelect: a topology-based feature selection method for industrial machine learning. 46-47 - Luigi Quaranta, Fabio Calefato, Filippo Lanubile:
Pynblint: a static analyzer for Python Jupyter notebooks. 48-49 - Jati H. Husen, Hironori Washizaki, Hnin Thandar Tun, Nobukazu Yoshioka, Yoshiaki Fukazawa, Hironori Takeuchi:
Traceable business-to-safety analysis framework for safety-critical machine learning systems. 50-51 - Jun Yajima, Maki Inui, Takanori Oikawa, Fumiyoshi Kasahara, Ikuya Morikawa, Nobukazu Yoshioka:
A new approach for machine learning security risk assessment: work in progress. 52-53
Training & learning
- Andrei Paleyes, Christian Cabrera, Neil D. Lawrence:
An empirical evaluation of flow based programming in the machine learning deployment context. 54-64 - Xiangzhe Xu, Hongyu Liu, Guanhong Tao, Zhou Xuan, Xiangyu Zhang:
Checkpointing and deterministic training for deep learning. 65-76 - Adriano Franci, Maxime Cordy, Martin Gubri, Mike Papadakis, Yves Le Traon:
Influence-driven data poisoning in graph-based semi-supervised classifiers. 77-87 - Ali Kanso, Kinshuman Patra:
Engineering a platform for reinforcement learning workloads. 88-89 - David Adkins, Bilal Alsallakh, Adeel Cheema, Narine Kokhlikyan, Emily McReynolds, Pushkar Mishra, Chavez Procope, Jeremy Sawruk, Erin Wang, Polina Zvyagina:
Method cards for prescriptive machine-learning transparency. 90-100
AI engineering practices
- Qinghua Lu, Liming Zhu, Xiwei Xu, Jon Whittle, Zhenchang Xing:
Towards a roadmap on software engineering for responsible AI. 101-112 - Samuli Laato, Teemu Birkstedt, Matti Mäntymäki, Matti Minkkinen, Tommi Mikkonen:
AI governance in the system development life cycle: insights on responsible machine learning engineering. 113-123 - Rimma Dzhusupova, Jan Bosch, Helena Holmström Olsson:
The goldilocks framework: towards selecting the optimal approach to conducting AI projects. 124-135 - Marcel Meesters, Petra Heck, Alexander Serebrenik:
What is an AI engineer?: an empirical analysis of job ads in The Netherlands. 136-144 - Anmol Singhal, Preethu Rose Anish, Pratik Sonar, Smita S. Ghaisas:
Data is about detail: an empirical investigation for software systems with NLP at core. 145-156
AI models & pipelines
- Akihito Yoshii, Susumu Tokumoto, Fuyuki Ishikawa:
Practical insights of repairing model problems on image classification. 157-158 - Erik Johannes Husom, Simeon Tverdal, Arda Goknil, Sagar Sen:
UDAVA: an unsupervised learning pipeline for sensor data validation in manufacturing. 159-169 - Birte Friesel, Olaf Spinczyk:
Black-box models for non-functional properties of AI software systems. 170-180 - Hamed Barzamini, Mona Rahimi, Murtuza Shahzad, Hamed Alhoori:
Improving generalizability of ML-enabled software through domain specification. 181-192 - Marcel Altendeitering, Julia Pampus, Felix Larrinaga, Jon Legaristi, Falk Howar:
Data sovereignty for AI pipelines: lessons learned from an industrial project at Mondragon corporation. 193-204
Smells
- Arumoy Shome, Luís Cruz, Arie van Deursen:
Data smells in public datasets. 205-216 - Haiyin Zhang, Luís Cruz, Arie van Deursen:
Code smells for machine learning applications. 217-228 - Harald Foidl, Michael Felderer, Rudolf Ramler:
Data smells: categories, causes and consequences, and detection of suspicious data in AI-based systems. 229-239
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