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ORCIDCAIN 2023: Melbourne, Australia
- 2nd IEEE/ACM International Conference on AI Engineering - Software Engineering for AI, CAIN 2023, Melbourne, Australia, May 15-16, 2023. IEEE 2023, ISBN 979-8-3503-0113-7
- Laurent Boué, Pratap Kunireddy, Pavle Subotic:
Automatically Resolving Data Source Dependency Hell in Large Scale Data Science Projects. 1-6 - Andrei Paleyes
, Siyuan Guo, Bernhard Schölkopf, Neil D. Lawrence:
Dataflow graphs as complete causal graphs. 7-12 - Hans-Martin Heyn, Khan Mohammad Habibullah, Eric Knauss, Jennifer Horkoff, Markus Borg, Alessia Knauss, Polly Jing Li:
Automotive Perception Software Development: An Empirical Investigation into Data, Annotation, and Ecosystem Challenges. 13-24 - Tim Yarally, Luis Cruz, Daniel Feitosa, June Sallou, Arie van Deursen:
Uncovering Energy-Efficient Practices in Deep Learning Training: Preliminary Steps Towards Green AI. 25-36 - Nicolás Cardozo, Ivana Dusparic, Christian Cabrera:
Prevalence of Code Smells in Reinforcement Learning Projects. 37-42 - Sagar Sen, Simon Myklebust Nielsen, Erik Johannes Husom, Arda Goknil, Simeon Tverdal, Leonardo Sastoque Pinilla:
Replay-Driven Continual Learning for the Industrial Internet of Things. 43-55 - Qiang Hu, Yuejun Guo, Maxime Cordy, Xiaofei Xie, Wei Ma, Mike Papadakis, Yves Le Traon:
Towards Understanding Model Quantization for Reliable Deep Neural Network Deployment. 56-67 - Sebastian Simon, Nikolay Kolyada, Christopher Akiki, Martin Potthast, Benno Stein, Norbert Siegmund:
Exploring Hyperparameter Usage and Tuning in Machine Learning Research. 68-79 - Yuta Ishimoto, Ken Matsui, Masanari Kondo, Naoyasu Ubayashi, Yasutaka Kamei:
An Initial Analysis of Repair and Side-effect Prediction for Neural Networks. 80-85 - Valentina Lenarduzzi, Minna Isomursu:
AI Living Lab: Quality Assurance for AI-based Health systems. 86-87 - Ilche Georgievski:
Conceptualising Software Development Lifecycle for Engineering AI Planning Systems. 88-89 - Emmanuel Iko-Ojo Simon, Melina C. Vidoni, Fatemeh H. Fard:
Algorithm Debt: Challenges and Future Paths. 90-91 - Armin Moin, Atta Badii, Stephan Günnemann, Moharram Challenger:
Enabling Machine Learning in Software Architecture Frameworks. 92-93 - Jati H. Husen, Hironori Washizaki, Hnin Thandar Tun, Nobukazu Yoshioka, Yoshiaki Fukazawa, Hironori Takeuchi, Hiroshi Tanaka, Kazuki Munakata:
Extensible Modeling Framework for Reliable Machine Learning System Analysis. 94-95 - Iva Krasteva, Boris Kraychev, Ensiye Kiyamousavi:
How Federated Machine Learning Helps Increase the Mutual Benefit of Data-Sharing Ecosystems. 96-97 - Lorena Poenaru-Olaru, Luis Cruz, Jan S. Rellermeyer, Arie van Deursen:
Maintaining and Monitoring AIOps Models Against Concept Drift. 98-99 - Iordanis Fostiropoulos, Bowman Brown, Laurent Itti:
Reproducibility Requires Consolidated Artifacts. 100-101 - Eduard Pinconschi, Sofia Reis, Chi Zhang, Rui Abreu, Hakan Erdogmus, Corina S. Pasareanu, Limin Jia:
Tenet: A Flexible Framework for Machine-Learning-based Vulnerability Detection. 102-103 - Boming Xia, Qinghua Lu, Harsha Perera, Liming Zhu, Zhenchang Xing, Yue Liu, Jon Whittle:
Towards Concrete and Connected AI Risk Assessment (C2AIRA): A Systematic Mapping Study. 104-116 - Arumoy Shome, Luís Cruz, Arie van Deursen:
Towards Understanding Machine Learning Testing in Practise. 117-118 - Petra Heck, Gerard Schouten:
Defining Quality Requirements for a Trustworthy AI Wildflower Monitoring Platform. 119-126 - András Schmelczer, Joost Visser:
Trustworthy and Robust AI Deployment by Design: A framework to inject best practice support into AI deployment pipelines. 127-138 - Leon Chemnitz, David Reichenbach, Hani Aldebes, Mariam Naveed, Krishna Narasimhan, Mira Mezini:
Towards Code Generation from BDD Test Case Specifications: A Vision. 139-144 - Marcel Grote, Justus Bogner:
A Case Study on AI Engineering Practices: Developing an Autonomous Stock Trading System. 145-157 - Muhammed Tawfiq Chowdhury, Jane Cleland-Huang:
Engineering Challenges for AI-Supported Computer Vision in Small Uncrewed Aerial Systems. 158-170 - Nadia Nahar, Haoran Zhang, Grace A. Lewis, Shurui Zhou, Christian Kästner:
A Meta-Summary of Challenges in Building Products with ML Components - Collecting Experiences from 4758+ Practitioners. 171-183 - Lukas Heiland, Marius Hauser, Justus Bogner:
Design Patterns for AI-based Systems: A Multivocal Literature Review and Pattern Repository. 184-196 - Marc Zeller, Martin Rothfelder, Cornel Klein:
safe.trAIn - Engineering and Assurance of a Driverless Regional Train. 197
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