1 citations · 2 across the 13 of their papers we have counts for
4 papers · 1 filter
Fault Localisation and Repair for DL Systems: An Empirical Study with LLMs
Jinhan Kim, Nargiz Humbatova, Gunel Jahangirova +2
Numerous Fault Localisation (FL) and repair techniques have been proposed to address faults in Deep Learning (DL) models. However, their effectiveness in practical applications rem…
PCLA: A Framework for Testing Autonomous Agents in the CARLA Simulator
Masoud Jamshidiyan Tehrani, Jinhan Kim, Paolo Tonella
Recent research on testing autonomous driving agents has grown significantly, especially in simulation environments. The CARLA simulator is often the preferred choice, and the auto…
Cross-site scripting adversarial attacks based on deep reinforcement learning: Evaluation and extension study
Samuele Pasini, Gianluca Maragliano, Jinhan Kim +1
Cross-site scripting (XSS) poses a significant threat to web application security. While Deep Learning (DL) has shown remarkable success in detecting XSS attacks, it remains vulner…
MuFF: Stable and Sensitive Post-training Mutation Testing for Deep Learning
Jinhan Kim, Nargiz Humbatova, Gunel Jahangirova +2
Rapid adoptions of Deep Learning (DL) in a broad range of fields led to the development of specialised testing techniques for DL systems, including DL mutation testing. However, ex…