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Background: Machine learning components are being deployed across nearly every business sector and their importance is continually growing. However, the engineering practices for building these systems remain poorly understood compared to those for conventional software systems. Objective: This work provides practical guidance to support architects in designing and implementing machine learning-intensive systems, and identifies areas where there are gaps in understanding and achievement. Method: Building on our prior research, we developed a checklist of quality concerns for architects of machine learning-intensive systems. This checklist was iteratively refined through expert interviews and subsequently validated in a workshop with experienced architects. Results: The main result of this work is a comprehensive list of 40 checks, organized into two main categories and 16 subcategories. Also, we present the results of a workshop where the importance and degree of achievement of each check was assessed by 25 practicing architects of ML-intensive systems. Conclusion: The findings of this study contribute to a better understanding of the unique challenges of MLintensive systems and offer initial guidance to practitioners, and researchers on areas where future work should be directed. The findings of this study offer valuable support to architects in addressing the unique challenges of ML-intensive systems and provide guidance to practitioners and researchers in terms of where future work should be focused.

Large language models are increasingly embedded in software development, yet most evaluations still treat them as one-shot generators for isolated tasks such as code completion or refactoring. In real workflows, however, artifacts such as application programming interfaces, data models, and database schemas co-evolve, and changes must propagate across representations without breaking consistency. When propagation fails, developers incur extra validation, retries, and manual repair, which increases latency and infrastructure cost and undermines sustainable operation. In this study, we ask whether large language models can preserve cross-artifact consistency under change in a round-trip workflow. We apply a controlled edit to one artifact, translate it to its coupled counterpart, and translate it back, then check whether the intended edit persists without drift (i.e., unintended semantic changes or syntactic invalidity). We instantiate this question by synchronizing class-oriented data models with relational database schemas. Using a curated dataset of paired models and schemas and a suite of controlled edit operations, we evaluate four large language model, GPT-5, Qwen3-Next-80B-A3B, DeepSeek V3, and Gemini 2.5, under a unified, reproducible protocol that measures (i) edit persistence, (ii) structural validity (parsability/loadability), and (iii) run-to-run consistency over repeated executions. Our results show that the models handle small, routine edits reliably, but they struggle when edits require structural reorganization. Gemini 2.5 is the most consistent across runs; DeepSeek V3 often preserves the intended semantics but occasionally produces unparsable outputs; Qwen3-Next-80B-A3B exhibits high variance; and GPT-5 often recognizes the change but fails to propagate it coherently through the coupled representation. We contribute a reproducible benchmark and evaluation framework for assessing LLM reliability under artifact co-evolution, together with empirical evidence of current limitations. Overall, the findings reveal a gap between detecting a change and propagating it coherently, underscoring the need for structural validation and human oversight to achieve dependable and cost-efficient LLM-assisted software evolution.

The rapid growth of the Internet of Things has led industries to quickly realize the value and opportunities offered by smart-manufacturing. Within smart-manufacturing, smart-troubleshooting has been introduced, which encompasses a structured set of activities and tools designed to collect failure information from heterogeneous connected devices, analyze it, and align it with appropriate troubleshooting instructions and fixes. In this paper, the general smart-troubleshooting framework is validated using real computerized numeric control (CNC) machine data, in which heterogeneous sensor and event logs are transformed through large-scale feature engineering into a unified time-indexed dataset. A LightGBM (Light Gradient Boosting Machine) model has been trained to predict upcoming faults, enabling early warnings and reducing unplanned downtime. The model has been validated using timeaware cross-validation through an embargo gap to prevent temporal leakage. The proposed approach has been evaluated on a real-world industrial dataset comprising 64 heterogeneous parameters and 1,583 recorded fault timestamps. Using a LightGBM-based baseline, our method achieves a rowlevel F1 score of ≈ 0.781 under time-aware cross-validation. More importantly, when shifting to event-level evaluation, the model attains a global operating point F1 score of ≈ 0.992, demonstrating near-perfect fault event detection with minimal false alarms. In summary, results of model evaluation on CNC machine data show reduced downtime and false alarms, while establishing a scalable foundation for resilient system management in complex industrial environments.

Digital twins, dynamic digital representations of physical systems, are emerging as transformative tools for enhancing crisis preparedness and resilience in critical societal sectors. By enabling real-time monitoring, simulation, and optimization, these technologies offer actionable insights to support proactive risk mitigation, efficient resource allocation, and continuous improvement of crisis response strategies. This study provides a comprehensive knowledge overview of digital twins, focusing on their applicability and impact in key sectors such as energy, healthcare, and transportation. Specifically, it examines the essential services most suited for digital twin adoption, the role of safety-critical data throughout their life-cycle, and their utility in identifying and mitigating risks within critical infrastructure. We employed a mixed-methods research design, combining systematic and gray literature reviews with expert interviews to integrate academic insights with practical perspectives. The findings reveal significant opportunities for digital twins to enhance operational efficiency, strategic planning, and crisis management. However, practical implementation remains in its infancy, with challenges related to cost, complexity, and limited real-world applications. In addition, this study provides actionable recommendations for stakeholders, emphasizing investment in digital twin technologies, robust data governance, and the development of standardized protocols. Future research directions include exploring applications of DTs in emerging sectors, such as crisis preparedness and societal resilience, advancing artificial intelligence integration, and adopting a system-of-systems perspective to address societal challenges comprehensively.

One of the most transformative developments today is the integration of generative artificial intelligence into the development of critical software-intensive cyber-physical systems. From autonomous vehicles to industrial robotics, these systems are entering a new era shaped by artificial intelligence-driven development and automation. In this paper, we consider software engineering, artificial intelligence, artificial intelligence ethics, and social aspects, to explore how such technologies can be harnessed safely, transparently, and with human values at the center. Our contributions include a vision for software engineering, guiding the engineering of future trustworthy safety-critical cyber-physical systems under the influence of generative artificial intelligence. We critically analyze how three established certification principles can be leveraged to cope with the societal and technical tensions introduced by generative artificial intelligence adoption, and propose a research and practice agenda to ensure that future cyber-physical systems development and operations cycle remain trustworthy, both from a system (hardware and software) and from a societal perspective. 

In resource-constrained Internet of Things environments, reducing data transmissions is essential for minimizing energy consumption, network load, and operational costs. Overly aggressive reduction may compromise accuracy, a critical factor in applications such as industrial control. This paper aims to offer practical guidance for selecting suitable data reduction techniques by experimentally evaluating three promising methods from common reduction categories: Data Filtering, Data Aggregation, and Data Prediction. We perform a parameter sweep for each algorithm across three real-world temperature scenarios: stable, rising, and fluctuating. Each configuration is evaluated in terms of data reduction percentage and accuracy, using Total Accumulated Deviation, Mean Absolute Deviation, and Maximum Deviation. Results show that Data Prediction generally achieves the highest accuracy across all scenarios, while Data Filtering tends to yield the greatest reduction at the expense of accuracy. However, all algorithms can be tuned to meet specific scenario demands or accuracy criteria, underscoring that no one-size-fits-all solution exists. We conclude that context-aware algorithm selection and parameter tuning are critical for effective Internet of Things data management.

Over the past three decades, automation has become a fundamental aspect of software development. Teams increasingly aim to automate activities across the entire development lifecycle, from requirements specification, to system maintenance. This move towards automation has been crucial for reducing development time and costs while embedding quality into every phase of the development process. The Workshop on Automated and verifiable Software sYstem DEvelopment (ASYDE) provided a forum to share and discuss innovative contributions to research and practice related to novel software engineering approaches to automated and verifiable development of software systems. The 7th edition of ASYDE took place on November 16th, 2025, co-located with the 40th IEEE/ACM International Conference on Automated Software Engineering (ASE) in Seoul, South Korea.

The integration of large language models into software systems is transforming capabilities such as natural language understanding, decision-making, and autonomous task execution. However, the absence of a commonly accepted software reference architecture hinders systematic reasoning about their design and quality attributes. This gap makes it challenging to address critical concerns like privacy, security, modularity, and interoperability, which are increasingly important as these systems grow in complexity and societal impact. In this paper, we describe our emerging results for a preliminary functional reference architecture as a conceptual framework to address these challenges and guide the design, evaluation, and evolution of large language model-integrated systems. We identify key architectural concerns for these systems, informed by current research and practice. We then evaluate how the architecture addresses these concerns and validate its applicability using three open-source large language model-integrated systems in computer vision, text processing, and coding.

Context: As cities grapple with increasing congestion, sustainability concerns, and the need for efficient mobility systems, Mobility Digital Twins (MoDTs) have emerged as promising technology for improving urban transportation. However, the development of MoDTs remains hindered by challenges such as structural complexity, data heterogeneity, lack of interoperability, and limited support for scalability, maintainability, and adaptability. Objective: This work aims to address these barriers by introducing a structured and systematic engineering framework that supports the design development of MoDT, reducing technical debt, development costs and human errors, while promoting long-term evolution. Methods: We propose a Model-Driven Engineering (MDE) approach that organizes the development of MoDTs through models at different levels of abstraction and adopts automated transformations from high-level specifications to executable code artifacts, supporting MoDT life-cycle. Results: The proposed approach is validated through its application in developing a MoDT for the city of Bologna, Italy. To support this, we introduce the M2DT tool, which automates the workflow from high-level models to software code artifacts. The resulting BoMoDT platform is built using open-source technologies and real mobility data. This case study demonstrates the feasibility and effectiveness of our approach, which, to our knowledge, is the first to apply a model-driven strategy for the entire MoDT development. A qualitative evaluation confirms that our framework addresses key challenges in MoDT development. Quantitative experiments further validate BoMoDT's ability to accurately reproduce and monitor real urban mobility conditions. Conclusion: The proposed approach offers a solid foundation for addressing MoDT development challenges. By combining automation with structured abstraction, it improves adaptability and maintainability while enabling scalable integration, helping make MoDTs more accessible for future urban system design. 

Translating programmable logic controller (PLC) programs into analyzable software artifacts is an important step toward enabling modern software testing and verification techniques in industrial settings. This paper presents a replication study in which we use PyLC+, a translation framework for IEC 61131-3 Function Block Diagrams (FBD), together with the Nagini verifier to perform functional correctness checks on industrial safety-critical logic. PyLC+ extracts block networks from PLCopen XML and generates executable Python models that preserve block semantics, signal flow, and scan-cycle behavior. We applied this workflow to a representative set of 13 industrial POUs from an electropneumatic brake control subsystem. All 13 POUs were verified: twelve using concrete or partially abstracted FBD models, and one using an abstract requirements-level specification whose 7-tuple specification initially exposed a bug in the Nagini verifier (a lack of support for tuples with more than six elements), which was fixed by the Nagini developers. The results show that a combination of automated extraction, manual semantic reconstruction, and contract-based specification is sufficient to verify realistic industrial logic at the POU level. We also discuss the abstraction decisions, the Nagini bug uncovered by one POU, and the remaining missing block semantics that constrain how far automation can be pushed in future versions of PyLC+.