21 papers · ranked by Valyu relevance
María-José Escalona, Nora Koch, Laura Garcia-Borgoñon, Juan Lara
Background The benefits of requirements traceability, such as improvements in software product and process quality, early testing, and software maintenance, are widely described in the literature. Requirements traceability is a critical, widely accepted practice. However, very often it is not applied for fear of the…
Hira Naveed, Chetan Arora, Hourieh Khalajzadeh, John Grundy + 1 more
'Omar Haggag'] Context: Machine Learning (ML) has become widely adopted as a component in many modern software applications. Due to the large volumes of data available, organizations want to increasingly leverage their data to extract meaningful insights and enhance business profitability. ML components enable…
Zahra Mardani Korani, Armin Moin, Alberto Rodrigues da Silva, João Carlos Ferreira + 1 more
'João Carlos Ferreira' 'Raffaele Bruno'] This paper reviews the literature on model-driven engineering (MDE) tools and languages for the internet of things (IoT). Due to the abundance of big data in the IoT, data analytics and machine learning (DAML) techniques play a key role in providing smart IoT applications. In…
Loli Burgueño, Davide Di Ruscio, Houari Sahraoui, Manuel Wimmer
Model-Driven Engineering (MDE) provides a huge body of knowledge of automation for many different engineering tasks, especially those involving transitioning from design to implementation. With the huge progress made on Artificial Intelligence (AI) techniques, questions arise for the future of MDE such as how existing…
Simon Raedler, Luca Berardinelli, Karolin Winter, Abbas Rahimi + 1 more
'Stefanie Rinderle‐Ma'] Background: Technical systems are becoming increasingly complex due to the increasing number of components, functions, and involvement of different disciplines. In this regard, Model-Driven Engineering techniques and practices tame complexity during the development process by using models as…
Goulão, Miguel, Amaral, Vasco + 2 more
Results: We identified 22 systematic literature reviews and mapping studies and the most relevant quality attributes addressed by each of those studies, in the context of MDE. Maintainability is clearly the most often studied and reported quality attribute impacted by MDE. 80 out of 83 research questions in the…
Hessa Alfraihi, Kevin Lano, Muhammad Suhail
In this study, we investigate the usability of Model-Driven Engineering (MDE) through interviews with fifteen practitioners from diverse roles (e.g., developers, researchers, architects) and domains, and with a range of expertise levels across academic and industrial software sectors, capturing in-depth perspectives on…
Fernandez-Candel, Carlos J., Garcia-Molina, Jesus + 8 more
Model-driven software engineering (MDE) techniques are not only useful in forward engineering scenarios, but can also be successfully applied to evolve existing systems. RAD (Rapid Application Development) platforms emerged in the nineties, but the success of modern software technologies motivated that a large number…
Jesús Sánchez Cuadrado
A key element of Model-Driven Engineering is the construction of domain-specific modelling environments to improve productivity and quality. In theory, dedicated technologies like EMF, ATL, Epsilon, Xtext, etc. would boost the construction of high-quality environments with a relatively modest effort by chaining the…
Ihsan Tolga Medeni, Metehan Ünal, Roberto Galizi, Bryan Bartley + 4 more
Large language models have transformed software engineering practices. However, generated artefacts are not always developer-friendly and may partially meet complex requirements. As the need to standardise, integrate, and develop tools in engineering biology increases, novel approaches are needed to create and maintain…
Anna Matuszyńska, Oliver Ebenhöh, Matias D. Zurbriggen, Daniel C. Ducat + 1 more
Synthetic biology designs and constructs new biological parts, devices and systems with predetermined functionalities. With the unlimited ability to synthesise any DNA and RNA and transfer it to almost any organism, we are at the dawn of a new era in which biology is being recreated in ways never before possible. It…
Authors not listed
Deriving versatile and robust mechanistic models from experimental data is a key challenge in engineering and natural sciences. This is especially true in chemical reaction engineering, where reactor manufacturers and operators increasingly pursue the development and maintenance of digital twins that rely on frequent…
Atefe Darabi, Zheming An, Muhammad Ali Al-Radhawi, William Cho + 2 more
This work explores the integration of machine learning (ML) and mechanistic models (MM). While ML has demonstrated remarkable success in data-driven modeling across engineering, biology, and other scientific fields, MM remain essential for their interpretability and capacity to extrapolate beyond observed conditions…
Kangjie Cao, Ting Zhang, Jueqiao Huang
In the field of engineering systems-particularly in underground drilling and green stormwater management-real-time predictions are vital for enhancing operational performance, ensuring safety, and increasing efficiency. Addressing this niche, our study introduces a novel LSTM-transformer hybrid architecture, uniquely…
Authors not listed
Digital twins are virtual companions for the design, scale-up, and control of chemical processes. Equipping digital twins with mechanistic models of their mirrored unit operation expands their range of applicability compared to pure data-driven models. As constructing mechanistic models requires time, effort, and…
Mahmoud Ibrahim, Viktor Rjabtšikov, Rolando Gilbert, Giambattista Gruosso
'Giambattista Gruosso'] Digital twin (DT) technology has been used in a wide range of applications, including electric vehicles. The DT platform provides a virtual representation or advanced simulation of a physical object in real-time. The implementation of DT on various aspects of EVs has recently transpired in…
David Medina-Ortiz, Ashkan Khalifeh, Hoda Anvari-Kazemabad, Mehdi D. Davari
Protein engineering using directed evolution and (semi)rational design has emerged as a powerful strategy for optimizing and enhancing enzymes or proteins with desired properties. Integrating artificial intelligence methods has further enhanced and accelerated protein engineering through predictive models developed in…
Jesús Picó, Andrés Arboleda-García, David R. Penas, Julio R. Banga + 2 more
Model-based design in synthetic biology is limited by the lack of quantitative, mechanistically interpretable biopart parameters that remain valid across genetic and physiological contexts. This limitation is particularly acute for transcriptional units, whose expression phenotypes emerge from nonlinear coupling…
Authors not listed
This conceptual paper introduces the Adaptive Multi-Resolution Modeling Framework (AMRMF), a novel technique designed to revolutionize chemical engineering by integrating multi-scale simulations, quantum-inspired algorithms, advanced uncertainty quantification, and Bayesian inference. The framework bridges theoretical…
Authors not listed
The increasing importance and predictive power of modern molecular modeling, driven by physics- and machine learning-based methods, necessitates a new collaborative architecture to replace the isolated, traditional model of software development. The traditional approach often led to redundant engineering effort, high…
Authors not listed
We present a multi-stage framework for predictive modeling that integrates automated feature engineering, selective dimensionality reduction, and targeted ensembling. Our pipeline begins with feature generation using a GPU-accelerated adaptation of AutoFeat, followed by variance-based pruning and LightGBM gain-based…