1 citations · 3 across the 10 of their papers we have counts for
10 papers
Revisiting "Revisiting Neuron Coverage for DNN Testing: A Layer-Wise and Distribution-Aware Criterion": A Critical Review and Implications on DNN Coverage Testing
Jinhan Kim, Nargiz Humbatova, Gunel Jahangirova +2
We present a critical review of Neural Coverage (NLC), a state-of-the-art DNN coverage criterion by Yuan et al. at ICSE 2023. While NLC proposes to satisfy eight design requirement…
TopoMap: A Feature-based 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 such inputs by feat…
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…
Real Faults in Deep Learning Fault Benchmarks: How Real Are They?
Gunel Jahangirova, Nargiz Humbatova, Jinhan Kim +2
As the adoption of Deep Learning (DL) systems continues to rise, an increasing number of approaches are being proposed to test these systems, localise faults within them, and repai…
An Empirical Study of Fault Localisation Techniques for Deep Learning
Nargiz Humbatova, Jinhan Kim, Gunel Jahangirova +2
With the increased popularity of Deep Neural Networks (DNNs), increases also the need for tools to assist developers in the DNN implementation, testing and debugging process. Sever…
muPRL: A Mutation Testing Pipeline for Deep Reinforcement Learning based on Real Faults
Deepak-George Thomas, Matteo Biagiola, Nargiz Humbatova +4
Reinforcement Learning (RL) is increasingly adopted to train agents that can deal with complex sequential tasks, such as driving an autonomous vehicle or controlling a humanoid rob…
Repairing DNN Architecture: Are We There Yet?
Jinhan Kim, Nargiz Humbatova, Gunel Jahangirova +2
As Deep Neural Networks (DNNs) are rapidly being adopted within large software systems, software developers are increasingly required to design, train, and deploy such models into…
DeepMetis: Augmenting a Deep Learning Test Set to Increase its Mutation Score
Vincenzo Riccio, Nargiz Humbatova, Gunel Jahangirova +1
Deep Learning (DL) components are routinely integrated into software systems that need to perform complex tasks such as image or natural language processing. The adequacy of the te…
Taxonomy of Real Faults in Deep Learning Systems
Nargiz Humbatova, Gunel Jahangirova, Gabriele Bavota +3
The growing application of deep neural networks in safety-critical domains makes the analysis of faults that occur in such systems of enormous importance. In this paper we introduc…