Mahyar Tourchi Moghaddam

Tiziano Santilli, Mina Alipour, Mahyar Tourchi Moghaddam

Digital nudging systems lack architectural guidance for translating behavioral science into software design. While research identifies nudge strategies and quality attributes, existing architectures fail to integrate multi-dimensional user modeling with ethical compliance as architectural concerns. We present an architecture that uses behavioral theory through explicit architectural decisions, treating ethics and fairness as structural guardrails rather than implementation details. A literature review synthesized 68 nudging strategies, 11 quality attributes, and 3 user profiling dimensions into architectural requirements. The architecture implements sequential processing layers with cross-cutting evaluation modules enforcing regulatory compliance. Validation with 13 software architects confirmed requirements satisfaction and domain transferability. An LLM-powered proof-of-concept in residential energy sustainability demonstrated feasibility through evaluation with 15 users, achieving high perceived intervention quality and measurable positive emotional impact. This work bridges behavioral science and software architecture by providing reusable patterns for adaptive systems that balance effectiveness with ethical constraints.

Oliver Aleksander Larsen, Mahyar T. Moghaddam

AI-augmented ecosystems (interconnected systems where multiple AI components interact through shared data and infrastructure) are becoming the architectural norm for smart cities, autonomous fleets, and intelligent platforms. Yet the architecture documentation frameworks practitioners rely on, arc42 and the C4 model, were designed for deterministic software and cannot capture probabilistic behavior, data-dependent evolution, or dual ML/software lifecycles. This gap carries regulatory consequence: the EU AI Act (Regulation 2024/1689) mandates technical documentation through Annex IV that no existing framework provides structured support for, with enforcement for high-risk systems beginning August 2, 2026. We present RAD-AI, a backward-compatible extension framework that augments arc42 with eight AI-specific sections and C4 with three diagram extensions, complemented by a systematic EU AI Act Annex IV compliance mapping. A regulatory coverage assessment with six experienced software-architecture practitioners provides preliminary evidence that RAD-AI increases Annex IV addressability from approximately 36% to 93% (mean rating) and demonstrates substantial improvement over existing frameworks. Comparative analysis on two production AI platforms (Uber Michelangelo, Netflix Metaflow) captures eight additional AI-specific concerns missed by standard frameworks and demonstrates that documentation deficiencies are structural rather than domain-specific. An illustrative smart mobility ecosystem case study reveals ecosystem-level concerns, including cascading drift and differentiated compliance obligations, that are invisible under standard notation.

Marco Aiello, Mina Alipour, Antonio Brogi et al.

The sustainability impacts of ICT systems are difficult to assess and govern due to structural complexity, fragmented measurement practices, and unclear responsibilities across system layers. We argue that these challenges cannot be addressed solely by metrics and motivate the need for a shared Green ICT reference framework that integrates sustainability across multiple perspectives and domains, lifecycle phases, and governance contexts. We present an initial framework developed within the Informatics Europe Green ICT Working Group as a first step towards a comprehensive reference framework.

Mahyar T. Moghaddam, Mina Alipour, Torben Worm et al.

Generative AI (GenAI) tools are increasingly integrated into software architecture research, yet the environmental impact of their computational usage remains largely undocumented. This study presents the first systematic audit of the carbon footprint of both the digital footprint from GenAI usage in research papers, and the traditional footprint from conference activities within the context of the IEEE International Conference on Software Architecture (ICSA). We report two separate carbon inventories relevant to the software architecture research community: i) an exploratory estimate of the footprint of GenAI inference usage associated with accepted papers within a research-artifact boundary, and ii) the conference attendance and operations footprint of ICSA 2025 (travel, accommodation, catering, venue energy, and materials) within the conference time boundary. These two inventories, with different system boundaries and completeness, support transparency and community reflection. We discuss implications for sustainable software architecture, including recommendations for transparency, greener conference planning, and improved energy efficiency in GenAI operations. Our work supports a more climate-conscious research culture within the ICSA community and beyond

Mahyar T. Moghaddam

High-stakes decision systems increasingly require structured justification, traceability, and auditability to ensure accountability and regulatory compliance. Formal arguments commonly used in the certification of safety-critical systems provide a mechanism for structuring claims, reasoning, and evidence in a verifiable manner. At the same time, generative artificial intelligence systems are increasingly integrated into decision-support workflows, assisting with drafting explanations, summarizing evidence, and generating recommendations. However, current deployments often rely on language models as loosely constrained assistants, which introduces risks such as hallucinated reasoning, unsupported claims, and weak traceability. This paper proposes a compliance-by-construction architecture that integrates Generative AI (GenAI) with structured formal argument representations. The approach treats each AI-assisted step as a claim that must be supported by verifiable evidence and validated against explicit reasoning constraints before it becomes part of an official decision record. The architecture combines four components: i) a typed Argument Graph representation inspired by assurance-case methods, ii) retrieval-augmented generation (RAG) to draft argument fragments grounded in authoritative evidence, iii) a reasoning and validation kernel enforcing completeness and admissibility constraints, and iv) a provenance ledger aligned with the W3C PROV standard to support auditability. We present a system design and an evaluation strategy based on enforceable invariants and worked examples. The analysis suggests that deterministic validation rules can prevent unsupported claims from entering the decision record while allowing GenAI to accelerate argument construction.

Francesco Daghero, Mahyar Tourchi Moghaddam, Mikkel Baun Kjærgaard

Industrial systems increasingly depend on Machine Learning (ML), and operate on heterogeneous nodes that must satisfy tight latency, energy, and memory constraints. Dynamic ML models, which reconfigure their computational footprint at runtime, promise high energy efficiency and lower average latency for modest accuracy tradeoffs; however, their deployment is complex due to the additional hyperparameters they rely on. These hyperparameters, controlling the accuracy versus average latency tradeoff, are often tuned on a calibration dataset that must match the test time distribution, an assumption that rarely holds in real-world scenarios, leading to suboptimal operational conditions, possibly below static models. We propose a two-tier adaptive architecture that co-optimizes model and system decisions. At the global level, a scheduler configures and deploys, for each edge node, a cascade of classifiers composed of lightweight specialized models and a generalist fallback, satisfying latency and memory constraints. At the node level, a local controller tracks data drifts and hardware resources, enabling or disabling specialized predictors (SP) to preserve high energy efficiency and avoid latency-constraint violations under varying conditions. This design allows longer operating times without forcing a global redeployment step, and enables efficient execution in case of an unreachable remote global controller. We evaluate the approach on two datasets under controlled distribution mismatch scenarios, showing average per-inference reductions of latency up to 2.45x and energy up to 2.86x, with less than 4% accuracy drop compared to static baselines. Our contributions are:(1) a budgeted SP-cascade formulation that preserves worst-case latency constraints;(2) a hierarchical controller that maintains efficiency under data and resource changes; and (3) an experimental evaluation on embedded hardware.

Leo Wilhelm Lierse, Mahyar Tourchi Moghaddam, Sebastian Werner

As carbon pricing mechanisms like the EU Emissions Trading System are set to increase prices of energy consumption, software architects face growing pressure to design applications that operate within financially predictable emission constraints. Existing approaches typically enforce rigid per-interval emission rates, which prove unsuitable in electrical grids with highly dynamic carbon intensity, which is common in grids with growing renewable energy adoption. We propose the use of emissions budgets, an approach that replaces fixed emission rates with time-bound budgets, enabling applications to dynamically save unused emission allowances during low carbon intensity periods and expend them during high carbon intensity periods. We describe emissions-aware applications using a MAPE-K feedback loop that continuously monitors application power consumption and grid carbon intensity, then adapts resource allocation through vertical scaling or migration to maintain long-term emission limits while maximizing performance. Through simulation using six weeks of real-world carbon intensity data from Germany, France, and Poland, we demonstrate that budget-based management improves task fulfillment by up to 36% in variable grids compared to fixed rates. Crucially, budgets achieve parity with fixed rates in stable grids, making them a safe replacement. We show that emissions budgets are a practical mechanism to balance environmental constraints, operational costs, and service quality when emissions directly translate to financial penalties.

Oliver Aleksander Larsen, Mahyar T. Moghaddam

Modern distributed systems integrate heterogeneous services, REST APIs with different schema versions, GraphQL endpoints, and IoT devices with proprietary payloads that suffer from persistent schema mismatches. Traditional static adapters require manual coding for every schema pair and cannot handle novel combinations at runtime. We present SAGAI-MID, a FastAPI-based middleware that uses large language models (LLMs) to dynamically detect and resolve schema mismatches at runtime. The system employs a five-layer pipeline: hybrid detection (structural diff plus LLM semantic analysis), dual resolution strategies (per-request LLM transformation and LLM-generated reusable adapter code), and a three-tier safeguard stack (validation, ensemble voting, rule-based fallback). We frame the architecture through Bass et al.'s interoperability tactics, transforming them from design-time artifacts into runtime capabilities. We evaluate SAGAI-MID on 10 interoperability scenarios spanning REST version migration, IoT-to-analytics bridging, and GraphQL protocol conversion across six LLMs from two providers. The best-performing configuration achieves 0.90 pass@1 accuracy. The CODEGEN strategy consistently outperforms DIRECT (0.83 vs 0.77 mean pass@1), while cost varies by over 30x across models with no proportional accuracy gain; the most accurate model is also the cheapest. We discuss implications for software architects adopting LLMs as runtime architectural components.

Oliver Aleksander Larsen, Rasmus Thorsen Larsen, Mahyar T. Moghaddam

Today's internet concentrates identity, payments, communication, and content hosting under a small number of corporate intermediaries, creating single points of failure, enabling censorship, and extracting economic rent from participants. We present BitSov, an architectural framework for sovereign internet infrastructure that composes existing decentralized technologies (Bitcoin, Lightning Network, decentralized storage, federated messaging, and mesh connectivity) into a unified, eight-layer protocol stack anchored to Bitcoin's base layer. The framework introduces three architectural patterns: (1) payment-gated messaging, where every transmitted message requires cryptographic proof of a Bitcoin payment, deterring spam through economic incentives rather than moderation; (2) timechain-locked contracts, which anchor subscriptions and licenses to Bitcoin block height (the timechain) rather than calendar dates; and (3) a self-sustaining economic flywheel that converts service revenue into infrastructure growth. A dual settlement model supports both on-chain transactions for permanence and auditability and Lightning micropayments for high-frequency messaging. As a position paper, we analyze the quality attributes, discuss open challenges, and propose a research agenda for empirical validation.

Mahyar T. Moghaddam, Henry Muccini, Julie Dugdale et al.

The Internet of Behaviors (IoB) puts human behavior at the core of engineering intelligent connected systems. IoB links the digital world to human behavior to establish human-driven design, development, and adaptation processes. This paper defines the novel concept by an IoB model based on a collective effort interacting with software engineers, human-computer interaction scientists, social scientists, and cognitive science communities. The model for IoB is created based on an exploratory study that synthesizes state-of-the-art analysis and experts interviews. The architecture of a real industry 4.0 manufacturing infrastructure helps to explain the IoB model and it's application. The conceptual model was used to successfully implement a socio-technical infrastructure for a crowd monitoring and queue management system for the Uffizi Galleries, Florence, Italy. The experiment, which started in the fall of 2016 and was operational in the fall of 2018, used a data-driven approach to feed the system with real-time sensory data. It also incorporated prediction models on visitors' mobility behavior. The system's main objective was to capture human behavior, model it, and build a mechanism that considers changes, adapts in real-time, and continuously learns from repetitive behaviors. In addition to the conceptual model and the real-life evaluation, this paper provides recommendations from experts and gives future directions for IoB to become a significant technological advancement in the coming few years.

Mahyar T. Moghaddam, Moamin B. Abughazala, Vittorio Cortellessa et al.

This paper highlights humans' social and mobility behaviors' role in the continuous engineering of sustainable socio-technical systems. Our approach relates the humans' characteristics and intentions with the system's goals, and models such interaction. Such a modeling approach aligns the architectural design and associated quality of service (QoS) with humans' quality of experience (QoE). We design a simulation environment that combines agent-based social simulation (ABSS) with architectural models generated through a model-driven engineering approach. Our modeling approach facilitates choosing the best architectural model and system configuration to enhance both the humans' and system's sustainability. We apply our approach to the Uffizi Galleries crowd management system. Taking advantage of real data, we model different scenarios that impact QoE. We then assess various architectural models with different SW/HW configurations to propose the optimal model based on different scenarios concerning QoS-QoE requirements.

Karthik Vaidhyanathan, Antonio Bruno, Eleonora Mendola et al.

Cultural heritage sites in Italy typically attract a large number of tourists every year. However, the lack of support for i) locating contents of interest; ii) discovering information on specific contents; and iii) ease of navigation within the heritage site; hinders the overall experience of the visitor. To this end, in this work, we present a Digital Object Space Management service developed as a part of the VASARI project. The service generates a digital twin (with 3D visualization) of a given cultural heritage site and further provides support for navigation and localization, thereby providing an immersive cultural experience to the visitor.

Mahyar Tourchi Moghaddam

Fires, earthquakes, floods, hurricanes, overcrowding, or and even pandemic viruses endanger human lives. Hence, designing infrastructures to handle possible emergencies has become an ever-increasing need. The safe evacuation of occupants from the building takes precedence when dealing with the necessary mitigation and disaster risk management. This thesis deals with designing an IoT system to provide safe and quick evacuation suggestions. The IoT-based evacuation system provides optimal evacuation paths that can be continuously updated based on run-time sensory data, so evacuation guidelines can be adjusted according to visitors occupants that evolve over time. This thesis makes the following main contributions: i) Addressing an up to date state of the art class for IoT architectural styles and patterns; ii) Proposing a set of self-adaptive IoT patterns and assessing their specific quality attributes (fault-tolerance, energy consumption, and performance); iii) Designing an IoT infrastructure and testing its performance in both real-time and design-time applications; iv) Developing a network flow algorithm that facilitates minimizing the time necessary to evacuate people from a scene of a disaster; v) Modeling various social agents and their interactions during an emergency to improve the IoT system accordingly; vi) Evaluating the system by using empirical and real case studies.