1 citations · 2 across the 13 of their papers we have counts for
13 papers
When Knowledge Changes: Metamorphic Testing of RAG Systems with Mutations
Jinhan Kim, Samuele Pasini, Paolo Tonella
Retrieval-Augmented Generation (RAG)-based LLM systems rely on external document corpora that can evolve and change over time. However, current evaluation methodologies (e.g., RAGA…
Testing Retrieval-Augmented Generation Systems with Chunk Coverage
Jinhan Kim, Samuele Pasini, Paolo Tonella
Retrieval-Augmented Generation (RAG)-based systems\footnote{For brevity, RAG-based systems are referred to as RAG systems throughout this paper.} are increasingly deployed in high-…
Detecting Trojaned DNNs via Spectral Regression Analysis
Samuele Pasini, Jinhan Kim, Paolo Tonella
Modern DNNs are repeatedly fine-tuned to incorporate new data and functionality. This evolutionary workflow introduces a security risk when updated data cannot be fully trusted, as…
Dynamic Deception: When Pedestrians Team Up to Fool Autonomous Cars
Masoud Jamshidiyan Tehrani, Marco Gabriel, Jinhan Kim +1
Many adversarial attacks on autonomous-driving perception models fail to cause system-level failures once deployed in a full driving stack. The main reason for such ineffectiveness…
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…
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…
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…
A Taxonomy of System-Level Attacks on Deep Learning Models in Autonomous Vehicles
Masoud Jamshidiyan Tehrani, Jinhan Kim, Rosmael Zidane Lekeufack Foulefack +2
The advent of deep learning and its astonishing performance has enabled its usage in complex systems, including autonomous vehicles. On the other hand, deep learning models are sus…
Evaluating and Improving the Robustness of Security Attack Detectors Generated by LLMs
Samuele Pasini, Jinhan Kim, Tommaso Aiello +3
Large Language Models (LLMs) are increasingly used in software development to generate functions, such as attack detectors, that implement security requirements. A key challenge is…