Darine Amayed

Imen Benzarti, Ikram Darif, Abderrahmane Leshob et al.

Human-centered Requirements Engineering (HC-RE) integrates user cognition, emotions, and social interactions into the RE process through contributions from disciplines such as psychology, cognitive science, design thinking, and human-computer interaction. Despite growing interest, how these multidisciplinary contributions are structured and why they remain fragmented across the RE lifecycle is not well understood. This systematic mapping study analyzes 56 primary studies across seven dimensions, including RE phases, user involvement techniques, contributing disciplines, and evaluation methods. Results show that 70\% of approaches involve multidisciplinary contributions, yet only 39% have been empirically evaluated and 48% address only the elicitation phase. A cross-study analysis reveals a structural separation between two parallel integration traditions: a Cognitive-Formal (C-F) pathway grounded in goal-based frameworks and formal modeling, and a Participatory-Iterative (P-I) pathway grounded in scenario-based frameworks and iterative design. Each pathway has developed complementary strengths, but their near-total disconnection explains the persistent lifecycle concentration and theory-practice gap observed in the corpus. The findings identify the absence of translation mechanisms between human-centered artifacts and formal RE specifications as the field's primary structural gap, provide a structured research agenda organized into four priority tiers, and establish the empirical foundation for Experience-Centered Requirements Engineering, a direction in which user experience is explicitly operationalized as a first-class concern in requirements specification.

Riadh Ben Chaabene, Darine Amayed, Mohamed Cheriet

Machine learning abilities have become a vital component for various solutions across industries, applications, and sectors. Many organizations seek to leverage AI-based solutions across their business services to unlock better efficiency and increase productivity. Problems, however, can arise if there is a lack of quality data for AI-model training, scalability, and maintenance. We propose a data-centric federated learning architecture leveraged by a public blockchain and smart contracts to overcome this significant issue. Our proposed solution provides a virtual public marketplace where developers, data scientists, and AI-engineer can publish their models and collaboratively create and access quality data for training. We enhance data quality and integrity through an incentive mechanism that rewards contributors for data contribution and verification. Those combined with the proposed framework helped increase with only one user simulation the training dataset with an average of 100 input daily and the model accuracy by approximately 4\%.