CAIN 2023
Mon 15 - Sat 20 May 2023 Melbourne, Australia
co-located with ICSE 2023
Mon 15 May 2023 20:00 - 20:15 at Virtual - Zoom for CAIN - Training & Learning Chair(s): Rrezarta Krasniqi

With the prevalence of software systems adopting neural network models, the quality assurance of these systems has become crucial. Hence, various studies have proposed repairing methods for neural network models so far to improve the quality of the models. While these methods are evaluated by researchers, it is difficult to tell whether they succeed in all models and datasets (i.e., all developers’ environments). Because these methods require many resources, such as execution times, failing to repair neural networks would cost developers their resources. Hence, if developers can know whether repairing methods succeed before adopting them, they could avoid wasting their resources. This paper proposes prediction models that predict whether repairing methods succeed in repairing neural networks using a small resource. Our prediction models predict repairs and side-effects of repairing methods, respectively. We evaluated our prediction models on a state-of-the-art repairing method Arachne on three datasets, Fashion-MNIST, CIFAR-10, and GTSRB, and found our prediction models achieved high performance, an average ROC-AUC of 0.931 and an average f1score of 0.880 for the side-effects and an average ROC-AUC of 0.768 and an average f1-score of 0.725 for the repairs.

Mon 15 May

Displayed time zone: Hobart change

19:00 - 20:30
Training & LearningPapers at Virtual - Zoom for CAIN
Chair(s): Rrezarta Krasniqi University of North Carolina at Charlotte

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19:00
20m
Long-paper
Replay-Driven Continual Learning for the Industrial Internet of Things
Papers
Sagar Sen , Simon Myklebust Nielsen University of Oslo, Norway, Erik Johannes Husom SINTEF Digital, Arda Goknil SINTEF Digital, Simeon Tverdal SINTEF Digital, Leonardo Sastoque Pinilla Centro de Fabricación Avanzada Aeronáutica (CFAA)
19:20
20m
Long-paper
Towards Understanding Model Quantization for Reliable Deep Neural Network Deployment
Papers
Qiang Hu University of Luxembourg, Yuejun GUo University of Luxembourg, Maxime Cordy University of Luxembourg, Luxembourg, Xiaofei Xie Singapore Management University, Wei Ma Nanyang Technological University, Singapore, Mike Papadakis University of Luxembourg, Luxembourg, Yves Le Traon University of Luxembourg, Luxembourg
19:40
20m
Long-paper
Exploring Hyperparameter Usage and Tuning in Machine Learning ResearchDistinguished paper Award Candidate
Papers
Sebastian Simon Leipzig University, Nikolay Kolyada , Christopher Akiki Leipzig University, Martin Potthast Leipzig University, Benno Stein Bauhaus-University Weimar, Norbert Siegmund Leipzig University
Pre-print
20:00
15m
Short-paper
An Initial Analysis of Repair and Side-effect Prediction for Neural Networks
Papers
Yuta Ishimoto Kyushu University, Ken Matsui Kyushu University, Masanari Kondo Kyushu University, Naoyasu Ubayashi Kyushu University, Yasutaka Kamei Kyushu University
Pre-print