6 papers
Uncertainty-Guided Label Rebalancing for CPS Safety Monitoring
John Ayotunde, Qinghua Xu, Guancheng Wang +1
Safety monitoring is essential for Cyber-Physical Systems (CPSs). However, unsafe events are rare in real-world CPS operations, creating an extreme class imbalance that degrades sa…
CAFD: Concept-Aware DNN Fault Detection using VLMs
Amin Abbasishahkoo, Mahboubeh Dadkhah, Lionel Briand
Fault detection for Deep Neural Networks (DNNs) has received increasing attention in recent years. While more advanced hybrid approaches have been proposed to combine multiple sour…
Engineering Resource-constrained Software Systems with DNN Components: a Concept-based Pruning Approach
Federico Formica, Andrea Rota, Aurora Francesca Zanenga +4
Deep Neural Networks (DNNs) are widely used by engineers to solve difficult problems that require predictive modeling from data. However, these models are often massive, with milli…
A Highly Efficient Diversity-based Input Selection for DNN Improvement Using VLMs
Amin Abbasishahkoo, Mahboubeh Dadkhah, Lionel Briand
Maintaining or improving the performance of Deep Neural Networks (DNNs) through fine-tuning requires labeling newly collected inputs, a process that is often costly and time-consum…
MetaSel: A Test Selection Approach for Fine-tuned DNN Models
Amin Abbasishahkoo, Mahboubeh Dadkhah, Lionel Briand +1
Deep Neural Networks (DNNs) face challenges during deployment due to covariate shift, i.e., data distribution shifts between development and deployment contexts. Fine-tuning adapts…
DiffGAN: A Test Generation Approach for Differential Testing of Deep Neural Networks for Image Analysis
Zohreh Aghababaeyan, Manel Abdellatif, Lionel Briand +1
Deep Neural Networks (DNNs) are increasingly deployed across applications. However, ensuring their reliability remains a challenge, and in many situations, alternative models with…