Alain Abran

This study investigates how large language models (LLMs), specifically GPT-4, can analyze, explain, and enrich concepts in the International Vocabulary of Metrology (VIM). It uses a white-box approach, in which “white-box” qualifies the input, which is fully known and traceable to a validated normative source, while the model itself remains opaque: the material supplied consisted only of VIM definitions and graphical representations of the relationships between VIM terms, together with the relationship types of the informative Annex A of the VIM. The outputs were analyzed qualitatively by structured expert cross-review against three criteria: fidelity to VIM concepts, terminological consistency, and explanatory quality. Across the seven cases examined (four topology diagrams and three VIM concepts), GPT-4 generated structured descriptions and rendered the hierarchical and functional relationships between concepts. Limitations were also observed: occasional non-normative synonyms and purely qualitative descriptions of quantitative relationships. Output variability, although not measured here, is a known LLM property that calls for expert review. Under the conditions tested, GPT-4 appears to be a promising semantic assistant for studying normative vocabularies, with the controlled input context supporting a traceable assessment of its capabilities and gaps. These observations also suggest future directions, which were not tested here, such as AI-assisted teaching, standardization, and terminological interoperability.

As quantum computing has progressed toward fault-tolerant implementations, quantum error correction (QEC) for quantum hardware and software has become a dominant concern. While conventional QEC measurements, such as qubit overhead, code distance, and error thresholds, quantify physical resource costs, they do not capture the software-level functionality introduced by error detection, correction, and fault-tolerant gate protocols. This study investigated the QEC contribution to the functional size of quantum software and demonstrated that this contribution can be systematically measured using the COSMIC Functional Size Measurement method. A gate-level sizing approach, known as Gates’ Occurrences, was used to quantify functional user requirements (FUR) associated with QEC processes, including logical encoding, syndrome extraction, and recovery operations. A prototype measurement tool, implemented in Qiskit (Version 0.7.2), enables the automated calculation of COSMIC Function Points (CFP) from annotated circuit representations. Case studies involving Bell state preparation, Grover’s search algorithm, and 15-to-1 magic state distillation revealed that QEC-related functionality can account for 70–85% of the total functional size, even under simplified encoding assumptions. The results underscore the importance of QEC for the functional size of quantum software. These findings offer a reproducible foundation for functional sizing and future fault-tolerant quantum applications.

Measuring the functional size of business intelligence (BI) and digital marketing analytics dashboard development can provide quantitative input for estimating the required time and cost of software development efforts. However, digital analytics practitioners currently lack practical guidance for planning dashboard development using measurement-based inputs that support reliable estimation models. Therefore, this study proposes the first Common Software Measurement International Consortium (COSMIC) ISO/IEC 19761 framework to measure the functional size of software user requirements of Power BI, Data Studio, and Tableau. An empirical study was conducted to measure the functional sizes of two datasets based on COSMIC. These functional sizes were considered inputs to several machine learning (ML) models to predict the effort required for BI dashboard development. The outcomes of these ML models showed that projects with consistent, low-variability effort should be prioritized when collecting or selecting data for training effort-estimation models and recommended the use of Stacking Regressor, Linear Regression, and Voting Regressor models for similar datasets because these models recorded the highest standardized accuracy for predicting BI development efforts with high generalization performance. These findings demonstrate the importance of applying COSMIC as a software engineering standard to BI and digital marketing analytics projects by offering measurements that allow for decisions informed by data rather than intuition or guesswork.

Rapid growth in quantum computing has created an urgent need to adapt traditional software engineering methodologies. Among these methodological challenges, software size measurement plays a central role in project estimation, quality assurance, and software engineering management. However, there is no consolidated understanding of how it should be applied to quantum software systems. This paper presents the first systematic literature review on quantum software size measurement, synthesizing existing approaches, and identifying key research gaps. An analysis of 16 studies shows that size measures span multiple abstraction levels, with most research focusing on adapting classical metrics, such as Lines of Code (LOC) and functional size, to quantum software systems. LOC continues to be used for sizing quantum code, while COSMIC Function Points (ISO 19761) have been extended to capture data movements in quantum components, enabling their application at the requirement, code, and circuit levels. At the specification and architectural design levels, the proposed measures include counts of quantum classes and methods as well as lines of architectural specifications. At the circuit level, quantum-specific constructs, such as qubits, gates, and measurements, are also incorporated. Additionally, software structure metrics, including cyclomatic complexity and Halstead metrics, are used to measure quantum software at both the code and design levels. Research output has grown since 2024, and is driven by academic groups, with minimal industrial participation. However, most metrics remain conceptual, lack empirical validation, and are not integrated into development life cycles, highlighting the immaturity and fragmentation of current proposals and the need for standardized frameworks.

Traceability mechanisms ensure transparency and accountability in distributed systems, where autonomous entities interact concurrently with shared resources without complete knowledge of the global state. Most existing approaches are descriptive: they record interactions after execution without constraining them according to the predefined usage rules. This limitation is important when shared resources must be accessed in compliance with global constraints under concurrent executions. We introduce directed traceability as a protocol-level mechanism that governs interaction admission according to usage constraints and produces a compliant execution trace by construction. Unlike descriptive mechanisms or locally-validated approaches such as smart contracts, which enforce only local constraints, directed traceability achieves compliance through execution-time validation against global state. We formalize the problem through a multi-agent model and establish three theoretical properties: (i) directed mechanisms guarantee zero violation rate; (ii) descriptive and locally-validated mechanisms exhibit structural violations under concurrency and global constraints; and (iii) directed traceability induces a bounded latency overhead of O(k · (n+m)), where k is the number of constraints, n the number of agents, and m the number of resources. Numerical evaluation through a distributed container-based implementation yields violation rates of 28%–35% (descriptive), 5%–10% (locally validated), and 0% (directed); a rise to 10% violations for local validation under global constraints; and a latency factor of approximately 100× for the directed mechanism under the tested container-based deployment. Compliance follows from protocol design rather than post-execution audit.

Recent advancements in artificial intelligence (AI) have strengthened the interaction between sports and digital technologies. However, unlike widely studied sports such as football and basketball, handball has received limited attention from the scientific community, despite its fast-paced nature and strategic importance. This study focuses on object detection in handball and targets key entities, such as players, referees, goalkeepers, and the ball. A comprehensive dataset was created through a collaborative annotation process, consisting of annotated images extracted from real handball games. The YOLOv8 model was then trained and evaluated on this dataset to assess its effectiveness in entity recognition. The proposed approach achieved an object detection accuracy of 86.8% on a relatively small held-out test set, providing an indicative first benchmark for the application of state-of-the-art machine learning models to handball. To the best of our knowledge, the dataset generated in this study is the first comprehensive collection of annotated handball images, providing a valuable resource for further research. By bridging sports analytics and computer vision, this study contributes to the advancement of performance assessment in handball. These exploratory results suggest potential directions for future real-time systems and practical applications, such as improved understanding of player performance, team dynamics, and strategic decision-making.

In recent years, several researchers have investigated the challenges of accessibility in e-government portals and have contributed to many proposals for improvements. However, no comprehensive review has been conducted on this topic. This study aimed to survey and synthesize the published work on the accessibility of e-government portals for people with disabilities. We carried out a review using a systematic mapping study (SMS) to compile previous findings and provide comprehensive state-of-the-art. The SMS collected studies published between January 2000 and March 2025 were identified using an automated search in five known databases. In total, 112 primary studies were selected. The results showed a notable increase in interest and research activities related to accessibility in e-government portals. Journals are the most widely used publication channel; studies have mainly focused on evaluation research and show a commitment to inclusivity. “AChecker” and “Wave validator” are the most used accessibility evaluation tools. The findings also identified various accessibility guidelines, with the most frequently referenced being the web content accessibility guidelines (WCAG). Based on this study, several key implications emerge for researchers, and addressing them would be beneficial for researchers to advance e-government website accessibility in a meaningful way.