20 citations · 64 across the 26 of their papers we have counts for
30 papers
Generalizable Robustness Testing of DNN-Based Robotic Navigation Systems via XAI-Guided Search
Khizra Sohail, Miren Illarramendi, Aitor Arrieta
**Context:** Deep Neural Networks (DNNs) increasingly control Cyber-Physical Systems (CPSs), yet small input perturbations can cause unsafe system-level behavior. Existing approach…
Search-Based Metamorphic Testing of Vision-Language Models in Autonomous Underwater Robotic Software
Muhammad Yousaf, Aitor Arrieta, Shaukat Ali +2
Our industry partner focuses on quality assurance for industrial systems across multiple domains, including maritime systems, such as overwater vessels and autonomous underwater ro…
Hype Meets Reality: Large Language Models as Mutators in Search-based Automated Program Repair of Simulink-Stateflow Models
Ayesha Irshad, Pablo Valle, Jon Ayerdi +1
Search-based Automated Program Repair (APR) techniques rely on carefully designed mutation operators to explore the space of candidate fixes. Recent advances in Large Language Mode…
Delta Debugging for Cyber-Physical Systems with Flaky Test Executions
Pablo Valle, Shaukat Ali, Aitor Arrieta
Simulation-based testing is widely used to validate Cyber-Physical Systems (CPSs), yet modern CPS simulators frequently exhibit non-deterministic (flaky) behavior, making failures…
RAG-TESTER: Automated End-to-End Testing of Retrieval-Augmented Large Language Models
Ange Maiztegi, Jon Ayerdi, Miren Illarramendi +1
Retrieval-Augmented Generation (RAG) enables Large Language Models (LLMs) to use external and domain-specific knowledge, but its reliability depends on the interaction between the…
MANGO: Automated Multi-Agent Test Oracle Generation for Vision-Language-Action Models
Pablo Valle, Shaukat Ali, Aitor Arrieta +1
Vision-Language-Action (VLA) models are emerging robotic control systems that integrate perception, language understanding, and action generation in a unified architecture. Existin…
VISOR: A Vision-Language Model-based Test Oracle for Testing Robots
Prasun Saurabh, Pablo Valle, Aitor Arrieta +2
Testing robots requires assessing whether they perform their intended tasks correctly, dependably, and with high quality, a challenge known as the test oracle problem in software t…
Assessing Vision-Language Models for Perception in Autonomous Underwater Robotic Software
Muhammad Yousaf, Aitor Arrieta, Shaukat Ali +2
Autonomous Underwater Robots (AURs) operate in challenging underwater environments, including low visibility and harsh water conditions. Such conditions present challenges for soft…
Metamorphic Testing of Vision-Language Action-Enabled Robots
Pablo Valle, Sergio Segura, Shaukat Ali +1
Vision-Language-Action (VLA) models are multimodal robotic task controllers that, given an instruction and visual inputs, produce a sequence of low-level control actions (or motor…
Exploring the Potential of Large Language Models in Simulink-Stateflow Mutant Generation
Pablo Valle, Shaukat Ali, Aitor Arrieta
Mutation analysis is a powerful technique for assessing test-suite adequacy, yet conventional approaches suffer from generating redundant, equivalent, or non-executable mutants. Th…
A Tool for Benchmarking Large Language Models' Robustness in Assessing the Realism of Driving Scenarios
Jiahui Wu, Chengjie Lu, Aitor Arrieta +1
In recent years, autonomous driving systems have made significant progress, yet ensuring their safety remains a key challenge. To this end, scenario-based testing offers a practica…
Vision Language Model-based Testing of Industrial Autonomous Mobile Robots
Jiahui Wu, Chengjie Lu, Aitor Arrieta +2
PAL Robotics, in Spain, builds a variety of Autonomous Mobile Robots (AMRs), which are deployed in diverse environments (e.g., warehouses, retail spaces, and offices), where they w…
Search-based Generation of Waypoints for Triggering Self-Adaptations in Maritime Autonomous Vessels
Karoline Nylænder, Aitor Arrieta, Shaukat Ali +1
Self-adaptation in maritime autonomous vessels (AVs) enables them to adapt their behaviors to address unexpected situations while maintaining dependability requirements. During the…
Evaluating Uncertainty and Quality of Vision-Language-Action-enabled Robots
Pablo Valle, Chengjie Lu, Shaukat Ali +1
Vision-Language-Action (VLA)-enabled robots integrate visual perception, natural language understanding, and action planning to interpret their environment, comprehend instructions…
Foundation Models for Software Engineering of Cyber-Physical Systems: the Road Ahead
Chengjie Lu, Pablo Valle, Jiahui Wu +4
Foundation Models (FMs), particularly Large Language Models (LLMs), are increasingly used to support various software engineering activities (e.g., coding and testing). Their adopt…
Red Teaming Contemporary AI Models: Insights from Spanish and Basque Perspectives
Miguel Romero-Arjona, Pablo Valle, Juan C. Alonso +7
The battle for AI leadership is on, with OpenAI in the United States and DeepSeek in China as key contenders. In response to these global trends, the Spanish government has propose…
Reinforcement Learning for Testing Interdependent Requirements in Autonomous Vehicles: An Empirical Study
Jiahui Wu, Chengjie Lu, Aitor Arrieta +1
Autonomous vehicles (AVs) make driving decisions without humans, making dependability assurance critical. Scenario-based testing is widely used to evaluate AVs under diverse condit…
o3-mini vs DeepSeek-R1: Which One is Safer?
Aitor Arrieta, Miriam Ugarte, Pablo Valle +2
The irruption of DeepSeek-R1 constitutes a turning point for the AI industry in general and the LLMs in particular. Its capabilities have demonstrated outstanding performance in se…
Early External Safety Testing of OpenAI's o3-mini: Insights from the Pre-Deployment Evaluation
Aitor Arrieta, Miriam Ugarte, Pablo Valle +2
Large Language Models (LLMs) have become an integral part of our daily lives. However, they impose certain risks, including those that can harm individuals' privacy, perpetuate bia…
ASTRAL: Automated Safety Testing of Large Language Models
Miriam Ugarte, Pablo Valle, José Antonio Parejo +2
Large Language Models (LLMs) have recently gained attention due to their ability to understand and generate sophisticated human-like content. However, ensuring their safety is para…
Assessing Quantum Extreme Learning Machines for Software Testing in Practice
Asmar Muqeet, Hassan Sartaj, Aitor Arrieta +6
Machine learning has been extensively applied for classical software testing activities such as test generation, minimization, and prioritization. Along the same lines, there has b…
Foundation Models for the Digital Twin Creation of Cyber-Physical Systems
Shaukat Ali, Paolo Arcaini, Aitor Arrieta
Foundation models are trained on a large amount of data to learn generic patterns. Consequently, these models can be used and fine-tuned for various purposes. Naturally, studying s…
Search-based Automated Program Repair of CPS Controllers Modeled in Simulink-Stateflow
Aitor Arrieta, Pablo Valle, Shaukat Ali
Stateflow models are widely used in the industry to model the high-level control logic of Cyber-Physical Systems (CPSs) in Simulink--the defacto CPS simulator. Many approaches exis…
Reality Bites: Assessing the Realism of Driving Scenarios with Large Language Models
Jiahui Wu, Chengjie Lu, Aitor Arrieta +2
Large Language Models (LLMs) are demonstrating outstanding potential for tasks such as text generation, summarization, and classification. Given that such models are trained on a h…