34 citations · 39 across the 6 of their papers we have counts for
6 papers
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…
Supporting System Testing with a Multi-Agent LLM-based Framework for Knowledge Graph Extraction: A Case Study with Ethernet Switch Systems
Rongqi Pan, Mahboubeh Dadkhah, Jean Baptiste Minani +3
Technical documents contain rich domain knowledge for automating downstream tasks such as system testing. While this paper focuses on Ethernet switch configuration manuals (ESCMs),…
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…
TEASMA: A Practical Methodology for Test Adequacy Assessment of Deep Neural Networks
Amin Abbasishahkoo, Mahboubeh Dadkhah, Lionel Briand +1
Successful deployment of Deep Neural Networks (DNNs) requires their validation with an adequate test set to ensure a sufficient degree of confidence in test outcomes. Although well…
DeepGD: A Multi-Objective Black-Box Test Selection Approach for Deep Neural Networks
Zohreh Aghababaeyan, Manel Abdellatif, Mahboubeh Dadkhah +1
Deep neural networks (DNNs) are widely used in various application domains such as image processing, speech recognition, and natural language processing. However, testing DNN model…