Yutaka Matsubara

Large language models (LLMs) have demonstrated remarkable capabilities in various software engineering tasks, such as code generation and debugging, because of their ability to translate between programming languages and natural languages. Existing studies have demonstrated the effectiveness of LLMs in code clone detection. However, two crucial issues remain unaddressed: the ability of LLMs to achieve comparable performance across different datasets and the consistency of LLMs' responses in code clone detection. To address these issues, we constructed seven code clone datasets and then evaluated five LLMs in four existing prompts with these datasets. The datasets were created by sampling code pairs using their Levenshtein ratio from two different code collections, CodeNet and BigCloneBench. Our evaluation revealed that although LLMs perform well in CodeNet-related datasets, with o3-mini achieving a 0.943 F1 score, their performance significantly decreased in BigCloneBench-related datasets. Most models achieved a high response consistency, with over 90\% of judgments remaining consistent across all five submissions. The fluctuations of the F1 score affected by inconsistency are also tiny; their variations are less than 0.03.

Efficient baggage handling is essential for smooth airport operations, directly impacting passenger satisfaction, airline performance, and logistical efficiency. With rising air travel demand, especially in Japan where labor shortages and limited space are critical issues, conventional Baggage Handling Systems (BHS) face increasing strain. This research proposes a compact Early Baggage Storage (EBS) system to address these challenges by optimizing baggage flow and minimizing delays. Unlike fixed-constraint models that assume uniform baggage arrival, the proposed simulationbased approach accounts for real-world fluctuations, such as passenger arrival patterns, enabling dynamic system responses. The study comprises three stages: model construction, simulation using AnyLogic (2024), and performance evaluation. The model replicates baggage flow from check-in to final loading, including sorting, screening via Explosive Detection System (EDS), and make-up area processing. Two EBS algorithms are analyzed: a conventional model and a proposed dynamic model that adjusts storage duration and baggage release intervals. The evaluation highlights the limitations of the conventional EBS, where smaller storage capacities cause congestion, and only larger capacities maintain smooth operations, though this comes with increased infrastructure costs. In contrast, the proposed EBS dynamically regulates baggage, improving subsystem balance and enabling smaller storage sizes to achieve comparable performance. This approach reduces congestion and infrastructure needs, making it a more efficient and cost-effective solution. Future work includes validating the proposed system in real airport settings and addressing make-up area delays through adaptive scheduling or buffer strategies to ensure broader applicability and operational robustness.

This study aims to explore how to improve the reliability of the next-generation mobility model—Mobility as a Service (MaaS) based on autonomous vehicles, with a particular focus on the system’s resilience to uncertainty. Currently, the application of reliability engineering in the field of smart mobility services is primarily concentrated on technical details, lacking unified standards and methods to enhance the reliability of service levels. This paper attempts to fill this research gap. In this study, we adopt a combined approach of system analysis and optimization algorithms. First, we design a system reliability analysis method by examining the potential discrepancies between the system’s capability to provide mobility services and stakeholder demands. Subsequently, we propose a reinforcement learning-based system service capacity optimization algorithm aimed at enhancing the system’s resilience at the service level to tackle challenges posed by uncertainty. To validate the effectiveness of the proposed method, we conduct a case study on a practical intelligent mobility service framework. Through system simulation, we generate and collect data on system service capacity, demand discrepancies, and uncertainty, as well as stakeholders’ expectations for the MaaS framework evaluation. Case studies and experimental data analysis confirm that the proposed resilience engineering approach effectively identifies potential risks in system service capacity and provides a compromise system resilience engineering solution in the context of conflicting stakeholder demands. To facilitate reproducibility and further research, the core code is available at https://github.com/zzs-code/MaaS-RE-MARL.git.

Software-Defined Vehicles (SDVs) consolidate safety-and non-safety-critical functions onto centralized platforms, forming mixed-criticality systems (MCSes) where applications like ADAS and infotainment coexist. While this reduces cost and complexity, it also introduces challenges in isolation, timing determinism, and safety certification. Lingua Franca (LF) offers deterministic coordination but lacks mechanisms for per-application monitoring and recovery. To address this, we (1) derive orchestration requirements from the “Safety First for Automated Driving” white paper, and (2) propose an LF-compatible framework with external monitors that detect faults and enable selective restarts of faulty applications without disrupting others. This work presents the first practical per-application failover solution for LF, improving the reliability of SDV mixed-criticality orchestration.

Blockchain and smart contracts are widely used in IoT access control to create decentralized, trustworthy environments for secure access and record management. However, their application introduces a dual challenge: The transparency of blockchain and the use of addresses as identifiers can expose account privacy. To tackle this issue, this paper proposes a blockchain-based IoT access control system that enhances account anonymity and preserves privacy, particularly regarding user behavior, habits, and access records through the use of zero-knowledge proofs. The system incorporates an access control mechanism that combines access control lists with capability-based access control, enabling ownership verification of access rights without disclosing identity information. To evaluate the system’s feasibility, we conduct experiments in a smart building scenario, including both qualitative comparisons with existing methods and quantitative analyses of performance in terms of time, space, and gas consumption. The results indicate that our scheme achieves the best time efficiency in the proof generation and authorization phases, completing them in just 7 and 10 s, respectively—representing half the time required by the second-best approach. These findings underscore the system’s superior cost efficiency and enhanced security compared to existing solutions.

The system of systems (SoS) is essential for integrating independent systems to meet increasing service demands. However, its growing complexity leads to disruptions that are difficult to predict and mitigate, making resilience analysis a critical aspect of SoS development. Resilience reflects an SoS’s ability to absorb and adapt to disruptions, yet its quantitative assessment remains challenging due to diverse stakeholders. To address this, we propose a model-based approach leveraging ArchiMate for SoS visualization and quantitative modeling, combined with a resilience indicator derived from model simulations. This indicator identifies critical constituent systems (CSs) and informs resilient design strategies. Using Mobility as a Service (MaaS) as a case study, we demonstrate how the proposed method can be implemented in a specific SoS.

The complexity of a System of Systems makes resilience one of its key attributes. Numerous studies have focused on the quantitative assessment of resilience by trailing indicators, yet discussions on resilience assurance through monitoring leading indicators remain scarce. Resilience assurance in SoS faces two major challenges: lack of structured argumentation work related to resilience and conflicts among multiple independent stakeholders. This paper first introduces a resilience argumentation approach based on STAMP (Systems-Theoretic Accident Model and Processes), then employs cooperative consensus process model to seek consensus on resilience assurance. Finally, under the requirements of the international standard IEC 62853 for open systems dependability, a consensus based resilience assurance framework is proposed. Two SoS case studies, Microgrid and Mobility as a Service, are used to demonstrate the application of the proposed approach.