Abstract is missing.
- What is software quality for AI engineers?: towards a thinning of the fogValentina Golendukhina, Valentina Lenarduzzi, Michael Felderer. 1-9 [doi]
- Exploring ML testing in practice: lessons learned from an interactive rapid review with axis communicationsQunying Song, Markus Borg, Emelie Engström, Håkan Ardö, Sergio Rico. 10-21 [doi]
- Quality assurance of generative dialog models in an evolving conversational agent used for Swedish language practiceMarkus Borg, Johan Bengtsson, Harald Österling, Alexander Hagelborn, Isabella Gagner, Piotr Tomaszewski. 22-32 [doi]
- MLOps: five steps to guide its effective implementationBeatriz M. A. Matsui, Denise H. Goya. 33-34 [doi]
- Towards a methodological framework for production-ready AI-based software componentsMarkus Haug, Justus Bogner. 35-36 [doi]
- Preliminary insights to enable automation of the software development process in software StartUps: an investigation study from the use of artificial intelligence and machine learningOlimar Teixeira Borges, Valentina Lenarduzzi, Rafael Prikladnicki. 37-38 [doi]
- Identification of out-of-distribution cases of CNN using class-based surprise adequacyMira Marhaba, Ettore Merlo, Foutse Khomh, Giuliano Antoniol. 39-40 [doi]
- Robust active learning: sample-efficient training of robust deep learning modelsYuejun Guo 0001, Qiang Hu, Maxime Cordy, Mike Papadakis, Yves Le Traon. 41-42 [doi]
- Structural causal models as boundary objects in AI system developmentHans-Martin Heyn, Eric Knauss. 43-45 [doi]
- TopSelect: a topology-based feature selection method for industrial machine learningHadil Abukwaik, Lefter Sula, Pablo Rodriguez. 46-47 [doi]
- Pynblint: a static analyzer for Python Jupyter notebooksLuigi Quaranta, Fabio Calefato, Filippo Lanubile. 48-49 [doi]
- Traceable business-to-safety analysis framework for safety-critical machine learning systemsJati H. Husen, Hironori Washizaki, Hnin Thandar Tun, Nobukazu Yoshioka, Yoshiaki Fukazawa, Hironori Takeuchi. 50-51 [doi]
- A new approach for machine learning security risk assessment: work in progressJun Yajima, Maki Inui, Takanori Oikawa, Fumiyoshi Kasahara, Ikuya Morikawa, Nobukazu Yoshioka. 52-53 [doi]
- An empirical evaluation of flow based programming in the machine learning deployment contextAndrei Paleyes, Christian Cabrera 0003, Neil D. Lawrence. 54-64 [doi]
- Checkpointing and deterministic training for deep learningXiangzhe Xu, Hongyu Liu, Guanhong Tao, Zhou Xuan, Xiangyu Zhang 0001. 65-76 [doi]
- Influence-driven data poisoning in graph-based semi-supervised classifiersAdriano Franci, Maxime Cordy, Martin Gubri, Mike Papadakis, Yves Le Traon. 77-87 [doi]
- Engineering a platform for reinforcement learning workloadsAli Kanso, Kinshuman Patra. 88-89 [doi]
- Method cards for prescriptive machine-learning transparencyDavid Adkins, Bilal Alsallakh, Adeel Cheema, Narine Kokhlikyan, Emily McReynolds, Pushkar Mishra, Chavez Procope, Jeremy Sawruk, Erin Wang, Polina Zvyagina. 90-100 [doi]
- Towards a roadmap on software engineering for responsible AIQinghua Lu 0001, Liming Zhu 0001, Xiwei Xu, Jon Whittle 0001, Zhenchang Xing. 101-112 [doi]
- AI governance in the system development life cycle: insights on responsible machine learning engineeringSamuli Laato, Teemu Birkstedt, Matti Mäntymäki, Matti Minkkinen, Tommi Mikkonen. 113-123 [doi]
- The goldilocks framework: towards selecting the optimal approach to conducting AI projectsRimma Dzhusupova, Jan Bosch, Helena Holmström Olsson. 124-135 [doi]
- What is an AI engineer?: an empirical analysis of job ads in The NetherlandsMarcel Meesters, Petra Heck, Alexander Serebrenik. 136-144 [doi]
- Data is about detail: an empirical investigation for software systems with NLP at coreAnmol Singhal, Preethu Rose Anish, Pratik Sonar, Smita S. Ghaisas. 145-156 [doi]
- Practical insights of repairing model problems on image classificationAkihito Yoshii, Susumu Tokumoto, Fuyuki Ishikawa. 157-158 [doi]
- UDAVA: an unsupervised learning pipeline for sensor data validation in manufacturingErik Johannes Husom, Simeon Tverdal, Arda Goknil, Sagar Sen. 159-169 [doi]
- Black-box models for non-functional properties of AI software systemsDaniel Friesel, Olaf Spinczyk. 170-180 [doi]
- Improving generalizability of ML-enabled software through domain specificationHamed Barzamini, Mona Rahimi, Murtuza Shahzad, Hamed Alhoori. 181-192 [doi]
- Data sovereignty for AI pipelines: lessons learned from an industrial project at Mondragon corporationMarcel Altendeitering, Julia Pampus, Felix Larrinaga, Jon Legaristi, Falk Howar. 193-204 [doi]
- Data smells in public datasetsArumoy Shome, Luís Cruz 0002, Arie van Deursen. 205-216 [doi]
- Code smells for machine learning applicationsHaiyin Zhang, Luís Cruz 0002, Arie van Deursen. 217-228 [doi]
- Data smells: categories, causes and consequences, and detection of suspicious data in AI-based systemsHarald Foidl, Michael Felderer, Rudolf Ramler. 229-239 [doi]