1 citations · 3 across the 10 of their papers we have counts for
3 papers · 1 filter
TopoMap: A Feature-based Semantic Discriminator of the Topographical Regions in the Test Input Space
Gianmarco De Vita, Nargiz Humbatova, Paolo Tonella
Testing Deep Learning (DL)-based systems is an open challenge. Although it is relatively easy to find inputs that cause a DL model to misbehave, the grouping of inputs by features…
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…
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…