| 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, Lus Cruz, Arie van Deursen |
| 2022 | Pynblint: a static analyzer for Python Jupyter notebooks. | Luigi Quaranta, Fabio Calefato, Filippo Lanubile |
| 2022 | An empirical evaluation of flow based programming in the machine learning deployment context. | Andrei Paleyes, Christian Cabrera, Neil D. Lawrence |
| 2022 | What is an AI engineer?: an empirical analysis of job ads in The Netherlands. | Marcel Meesters, Petra Heck, Alexander Serebrenik |
| 2022 | MLOps: five steps to guide its effective implementation. | Beatriz M. A. Matsui, Denise H. Goya |
| 2022 | Identification of out-of-distribution cases of CNN using class-based surprise adequacy. | Mira Marhaba, Ettore Merlo, Foutse Khomh, Giuliano Antoniol |
| 2022 | AI governance in the system development life cycle: insights on responsible machine learning engineering. | Samuli Laato, Teemu Birkstedt, Matti Mntymki, Matti Minkkinen, Tommi Mikkonen |
| 2022 | Engineering a platform for reinforcement learning workloads. | Ali Kanso, Kinshuman Patra |
| 2022 | UDAVA: an unsupervised learning pipeline for sensor data validation in manufacturing. | Erik Johannes Husom, Simeon Tverdal, Arda Goknil, Sagar Sen |
| 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 | Structural causal models as boundary objects in AI system development. | Hans-Martin Heyn, Eric Knauss |
| 2022 | Towards a methodological framework for production-ready AI-based software components. | Markus Haug, Justus Bogner |
| 2022 | What is software quality for AI engineers?: towards a thinning of the fog. | Valentina Golendukhina, Valentina Lenarduzzi, Michael Felderer |
| 2022 | Black-box models for non-functional properties of AI software systems. | Birte Friesel, Olaf Spinczyk |
| 2022 | Influence-driven data poisoning in graph-based semi-supervised classifiers. | Adriano Franci, Maxime Cordy, Martin Gubri, Mike Papadakis, Yves Le Traon |
| 2022 | Data smells: categories, causes and consequences, and detection of suspicious data in AI-based systems. | Harald Foidl, Michael Felderer, Rudolf Ramler |
| 2022 | The goldilocks framework: towards selecting the optimal approach to conducting AI projects. | Rimma Dzhusupova, Jan Bosch, Helena Holmstrm Olsson |
| 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 | Quality assurance of generative dialog models in an evolving conversational agent used for Swedish language practice. | Markus Borg, Johan Bengtsson, Harald sterling, Alexander Hagelborn, Isabella Gagner, Piotr Tomaszewski |
| 2022 | Improving generalizability of ML-enabled software through domain specification. | Hamed Barzamini, Mona Rahimi, Murtuza Shahzad, Hamed Alhoori |
| 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 | 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 | TopSelect: a topology-based feature selection method for industrial machine learning. | Hadil Abukwaik, Lefter Sula, Pablo Rodriguez |
| 2022 | Robust active learning: sample-efficient training of robust deep learning models. | Yuejun Guo, Qiang Hu, Maxime Cordy, Mike Papadakis, Yves Le Traon |