20 citations · 29 across the 21 of their papers we have counts for
22 papers
PCLA-Bridge: A Framework for Cross-Domain Deployment of CARLA Agents
Masoud Jamshidiyan Tehrani, Jinhan Kim, Paolo Tonella
Most high-performing end-to-end autonomous driving agents (ADAs) are developed for the CARLA Leaderboard, tightly coupling them to CARLA's APIs, sensor layouts, coordinate systems,…
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…
Bridging Research and Practice in Simulation-based Testing of Industrial Robot Navigation Systems
Sajad Khatiri, Francisco Eli Vina Barrientos, Maximilian Wulf +2
Ensuring robust robotic navigation in dynamic environments is a key challenge, as traditional testing methods often struggle to cover the full spectrum of operational requirements.…
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…
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…
When Uncertainty Leads to Unsafety: Empirical Insights into the Role of Uncertainty in Unmanned Aerial Vehicle Safety
Sajad Khatiri, Fatemeh Mohammadi Amin, Sebastiano Panichella +1
Despite the recent developments in obstacle avoidance and other safety features, autonomous Unmanned Aerial Vehicles (UAVs) continue to face safety challenges. No previous work inv…
Improving the Readability of Automatically Generated Tests using Large Language Models
Matteo Biagiola, Gianluca Ghislotti, Paolo Tonella
Search-based test generators are effective at producing unit tests with high coverage. However, such automatically generated tests have no meaningful test and variable names, makin…
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…
Adaptive Random Testing with Q-grams: The Illusion Comes True
Matteo Biagiola, Robert Feldt, Paolo Tonella
Adaptive Random Testing (ART) has faced criticism, particularly for its computational inefficiency, as highlighted by Arcuri and Briand. Their analysis clarified how ART requires a…
Efficient Domain Augmentation for Autonomous Driving Testing Using Diffusion Models
Luciano Baresi, Davide Yi Xian Hu, Andrea Stocco +1
Simulation-based testing is widely used to assess the reliability of Autonomous Driving Systems (ADS), but its effectiveness is limited by the operational design domain (ODD) condi…
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…
GenMorph: Automatically Generating Metamorphic Relations via Genetic Programming
Jon Ayerdi, Valerio Terragni, Gunel Jahangirova +2
Metamorphic testing is a popular approach that aims to alleviate the oracle problem in software testing. At the core of this approach are Metamorphic Relations (MRs), specifying pr…