FESTIM is an open-source finite element framework for modelling the transport of hydrogen isotopes in materials. This paper presents FESTIM v2.0, a major release that broadens the framework's physical scope and software infrastructure. The new formulation supports fully coupled multi-species transport, advanced trapping and reaction networks, isotope exchange, decay, advection, and generalised treatments of interfaces and boundaries. FESTIM v2.0 is built on DOLFINx, providing improved scalability, interoperability, and long-term sustainability. Performance benchmarks on representative multi-material diffusion problems show speedups of up to approximately 15 & times; relative to FESTIM v1, enabling more efficient engineering-scale, multiphysics simulations. The framework is designed for applications ranging from laboratory-scale permeation experiments to component-level analyses relevant to fusion fuel-cycle technologies. Together, these advances establish FESTIM v2.0 as a versatile and efficient platform for hydrogen transport modelling in complex materials systems.
Software Engineering (SE) research involving the use of Large Language Models (LLMs) has introduced several new challenges related to rigour in benchmarking, contamination, replicability, and sustainability. In this paper, we invite the research community to reflect on how these challenges are addressed in SE. Our results provide a structured overview of current LLM-based SE research at ICSE, highlighting both encouraging practices and persistent shortcomings. We conclude with recommendations to strengthen benchmarking rigour, improve replicability, and address the financial and environmental costs of LLM-based SE.
The brain's distributed architecture has inspired numerous artificial intelligence (AI) systems, particularly through its neocortical organization. However, current AI approaches largely overlook a crucial aspect of biological intelligence: active sensing - the deliberate movement of sensory organs to explore the environment. To explore how sensor movement impacts behavior in image classification tasks, we introduce the Active Neural Cellular Automata (ANCA), a neocortex-inspired model with movable sensors. Active sensing naturally emerges in the ANCA, with belief-informed exploration and attentive behavior to salient information, without adding explicit attention mechanisms. We show that active sensing simplifies classification tasks. Moreover, active sensing lets the ANCA be smaller than the image size without losing information, which makes it highly scalable. We show that the ANCA maintains over 90% accuracy zero-shot as the system size is increased or decreased on a 3-class MNIST task. This scalability enables fault tolerance on the same task, maintaining over 90% accuracy with up to 70% silenced sensors, a scenario where traditional architectures fail. Overall, our work provides insight to how distributed architectures can interact with movement, opening new avenues for adaptive AI systems in embodied agents.
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 commands) enabling a robot to execute the requested task in the physical environment. These systems face the test oracle problem from multiple perspectives. On the one hand, a test oracle must be defined for each instruction prompt, which is a complex and non-generalizable approach. On the other hand, current state-of-the-art oracles typically capture symbolic representations of the world (e.g., robot and object states), enabling the correctness evaluation of a task, but fail to assess other critical aspects, such as the quality with which VLA-enabled robots perform a task. In this paper, we explore whether Metamorphic Testing (MT) can alleviate the test oracle problem in this context. To do so, we propose two metamorphic relation patterns and five metamorphic relations to assess whether changes to the test inputs impact the original trajectory of the VLA-enabled robots. An empirical study involving five VLA models, two simulated robots, and four robotic tasks shows that MT can effectively alleviate the test oracle problem by automatically detecting diverse types of failures, including, but not limited to, uncompleted tasks. More importantly, the proposed MRs are generalizable, making the proposed approach applicable across different VLA models, robots, and tasks, even in the absence of test oracles.
With the increasing breaches and security threats that endanger health data, ensuring patients' privacy is essential. To that end, the research community has proposed various privacy-preserving approaches based on cryptography, hashing, or ledger technologies for alleviating health data vulnerability. To establish a comprehensive understanding of health data privacy risks, and the benefits and limitations of existing privacy-preserving approaches, we perform a detailed review of existing work and distill 10 distinct privacy concerns occurring in a health data lifecycle. Furthermore, we classify existing approaches based on their applicability to particular privacy concerns occurring at a particular lifecycle stage. Finally, we propose a taxonomy of techniques used for privacy preservation in healthcare and triangulate those techniques with the lifecycle stages and concerns. Our review indicates heavy usage of cryptographical techniques in this domain. However, we have also found that healthcare systems have special requirements that require novel cryptographic techniques and security schemes to address special needs. Therefore, we identify several future research directions to mitigate the security challenges for privacy preservation in health data management.
