Artículos (Lenguajes y Sistemas Informáticos)

URI permanente para esta colecciónhttps://hdl.handle.net/11441/11392

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Gintegrator: Enhancing biological sequences data integration with real-time identifier translation
(Elsevier, 2025) Sola Espinosa, Fernando Luis; Ayala Hernández, Daniel; Pulido, Marina R.; Ayala, Rafael; López Cerero, Lorena; Hernández Salmerón, Inmaculada Concepción; Ruiz Cortés, David; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; Universidad de Sevilla. Departamento de Microbiología; MICIU/AEI/ 10.13039/501100011033; Spanish Ministry of Science and Innovation (Health Institute Carlos III); Spanish Ministry of Universities
Advancements in genomic and proteomic sequencing, along with molecular biology techniques, have led to the generation of vast amounts of sequence data stored in different collaborative databases. The integration of these heterogeneous data sources holds immense potential for advancing our understanding of biological systems and processes, although it presents several challenges due to inconsistencies in formats and annotations. To address this, we introduce Gintegrator, a web application that streamlines the process of translating gene and protein identifiers across major sequence databases such as NCBI, UniProt and KEGG. By introducing the use of identical or similar genes and proteins in the translation workflow, and performing real-time queries to access the most recent data, Gintegrator enhances both the accuracy and success rate of identifier mapping, while also facilitating efficient and reliable biological data integration and analysis for researchers.
SpaceRL — A reinforcement learning-based knowledge graph driver
(Elsevier, 2025) Bermudo Bayo, Miguel; Ayala Hernández, Daniel; Hernández Salmerón, Inmaculada Concepción; Ruiz Cortés, David; Toro Bonilla, Miguel; Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; Spanish Ministry of Science, Innovation and Universities
Knowledge Graphs are powerful data structures used by large IT companies and the scientific community alike. They aid in the representation of related information by means of nodes connected through links indicating types of relations. These graphs are used as the basis for several smart applications, such as question answering or product recommendation. However, they are built in an automated unsupervised way, which leads to gaps in information, usually in the form of missing links between related entities in the original data source, which have to be added later by completion techniques. SpaceRL is an end-to-end Python framework designed for the generation of reinforcement learning (RL) agents, which can be used to complete knowledge graphs through link discovery. The purpose of the generated agents is to help identify missing links in a knowledge graph by finding paths that implicitly connect two nodes, incidentally providing a reasoned explanation for the inferred new link. The generation of such agents is a complex task, even more so for a non-expert user. SpaceRL is meant to overcome these limitations by providing a flexible set of tools designed with a wide variety of customization options, in order to adapt to different users’ needs, while also including a variety of state-of-the-art RL algorithms and several embedding models that can be combined to optimize the agents performance. Furthermore, SpaceRL offers different interfaces to make it available either locally (programmatically or via a GUI), or through an OpenAPI-compliant REST API.
Handling Non-determinism in Spiking Neural P Systems: Algorithms and Simulations
(IO Press, 2019) Carandang, Jym Paul; Cabarle, Francis George C.; Adorna, Henry Natividad; Hernandez, Nestine Hope S.; Martínez del Amor, Miguel Ángel; Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
Spiking Neural P system is a computing model inspired on how the neurons in a living being are interconnected and exchange information. As a model in embrane computing, it is a non-deterministic and massively-parallel system. The latter makes GPU a good candidate for ac celerating the simulation of these models. A matrix representation for systems with and without delay have been previously designed, and algorithms for simulating them with deterministic sys tems was also developed. So far, non-determinism has been problematic for the design of parallel simulators. In this work, an algorithm for simulating non-deterministic spiking neural P system with delays is presented. In order to study how the simulations get accelerated on a GPU, this algorithm was implemented in CUDA and used to simulate non-uniform and uniform solutions to the Subset Sum problem as a case study. The analysis is completed with a comparison of time and space resources in the GPU of such simulations.
Steps toward a homogenization procedure for spiking neural P systems
(Elsevier, 2024) de la Cruz, Ren Tristan A.; Cabarle, Francis George C.; Adorna, Henry N.; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
A spiking neural P system (SN P system) is a model of computation inspired by the mechanism of a network of spiking neurons. A homogeneous SN P system is an SN P system whose neurons have identical rule set. In this work, we introduced an algorithm that, under certain condition, transforms a non-homogeneous SN P system Π to a homogeneous SN P system Π′ that computes the same set as the system Π. Two transformation operations called neuron translation and neuron scaling are introduced which are then used by the homogenization algorithm.
Pricing4APIs: A rigorous model for RESTful API pricings
(Elsevier, 2024) Fresno Aranda, Rafael; Fernández Montes, Pablo; Gámez Díaz, Antonio; Durán Toro, Amador; Ruiz Cortés, Antonio; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; MCIN/AEI/10.13039/ 501100011033/FEDER,UE; MCIN/AEI/10.13039/501100011033
APIs are increasingly becoming new business assets for organizations and consequently, API functionality and its pricing should be precisely defined for customers. Pricing is typically composed by different plans that specify a range of limitations, e.g., a Free plan allows 100 monthly requests while a Gold plan has 10 000 requests per month. In this context, the OpenAPI Specification (OAS) has emerged to model the functional part of an API, becoming a de facto industry standard and boosting a rich ecosystem of vendor-neutral tools to assist API providers and consumers. In contrast, there is no proposal for modeling API pricings (i.e., their plans and limitations) and this lack hinders the creation of tools that can leverage this information. To deal with this gap, this paper presents a pricing modeling framework that includes: (a) Pricing4APIs model, a comprehensive and rigorous model of API pricings, along SLA4OAI, a serialization that extends OAS; (b) an operation to validate the description of API pricings, with a toolset (sla4oai-analyzer) that has been developed to automate this operation. Additionally, we analyzed 268 real-world APIs to assess the expressiveness of our proposal and created a representative dataset of 54 pricing models to validate our framework.
Técnicas Big Data para la predicción de la demanda y precio eléctrico
(Ministerio de Industria, Turismo y Comercio, 2024) Melgar García, Laura; Torres Maldonado, José Francisco; Troncoso, Alicia; Riquelme Santos, José Cristóbal; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
En la actualidad, la electricidad es un pilar indispensable en el día a día de la sociedad. De hecho, es calificada como uno de los indicadores más importantes para medir el nivel tecnológico e industrial de desarrollo de un país [1]. La concienciación sobre su uso de forma sostenible y responsable está siendo uno de los retos más importantes de las últimas décadas. Concretamente, en términos de energía, en [2] se describe la eficiencia energética como el factor de mitigación principal para rebajar el crecimiento del consumo energético, siendo éste crucial para el desarrollo sostenible.
Benchmarking machine learning approaches to predict radiation-induced toxicities in lung cancer patients
(Elsevier Ireland, 2023-07) Núñez-Benjumea, F.J.; González-García, Sara; Moreno-Conde, A.; Riquelme Santos, José Cristóbal; López-Guerra, J.L.; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; Unión Europea; Conserjería de Salud Junta de Andalucía
Background and purpose: Radiation-induced toxicities are common adverse events in lung cancer (LC) patients undergoing radiotherapy (RT). An accurate prediction of these adverse events might facilitate an informed and shared decision-making process between patient and radiation oncologist with a clearer view of life-balance implications in treatment choices. This work provides a benchmark of machine learning (ML) approaches to predict radiation-induced toxicities in LC patients built upon a real-world health dataset based on a generalizable methodology for their implementation and external validation. Materials and Methods: Ten feature selection (FS) methods were combined with five ML-based classifiers to predict six RT-induced toxicities (acute esophagitis, acute cough, acute dyspnea, acute pneumonitis, chronic dyspnea, and chronic pneumonitis). A real-world health dataset (RWHD) built from 875 consecutive LC patients was used to train and validate the resulting 300 predictive models. Internal and external accuracy was calculated in terms of AUC per clinical endpoint, FS method, and ML-based classifier under analysis. Results: Best performing predictive models obtained per clinical endpoint achieved comparable performances to methods from state-of-the-art at internal validation (AUC ≥ 0.81 in all cases) and at external validation (AUC ≥ 0.73 in 5 out of 6 cases). Conclusion: A benchmark of 300 different ML-based approaches has been tested against a RWHD achieving satisfactory results following a generalizable methodology. The outcomes suggest potential relationships between underrecognized clinical factors and the onset of acute esophagitis or chronic dyspnea, thus demonstrating the potential that ML-based approaches have to generate novel data-driven hypotheses in the field.
E-SCORE: A web-based tool for security requirements engineering
(Elsevier, 2024-05) Hnaini, Hiba; Mazo, Raúl; Champeau, Joël; Vallejo, Paola; Galindo Duarte, José Ángel; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
As digital systems continue to grow in popularity, they also become more vulnerable to various forms of attacks with various motives, including financial gain and political influence. In response, engineers must consider system security from the design phase. However, defining security requirements at this stage can be challenging. To address this challenge, we propose E-SCORE, a web-based tool that streamlines the security requirements engineering process. E-SCORE implements the SCORE (Security Criteria Ontology for security Requirements Engineering) (i) to suggest security mechanisms and additional criteria to enhance security coverage and (ii) to facilitate security analysis of the system. An example of banking system usage is provided. Through our approach, we could define ten additional security requirements for a single requirement. Therefore, E-SCORE offers a valuable resource for engineers to ensure the security of digital systems across various domains
Evaluation of synthetically generated traces towards a data-centre digital twin
(European Council for Modelling and Simulation, 2023) Fernández Montes González, Alejandro; Fernández Cerero, Damián; Jakóbik, Agnieszka; Bermejo, Belén; Juiz, Carlos; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
Several approaches exist to generate synthetic data centre traces for various purposes: from augmenting operational traces for data centre simulators and digital twins to forecasting incoming workload to improve data centre behaviour. The evaluation of the quality of synthetically generated multivariate time-series datasets, such as those related to data-centre traces, is not a trivial task, since complex patterns and correlation between variables may be present. This paper proposes a new multivariate time-series evaluation framework that computes a set of metrics and figures that can be used to measure the quality of synthetically generated data-centre traces. We then employ the proposed tool to compare two synthetic data centre traces with the original trace and assess their quality. These synthetic traces have been generated by means of Generative Adversarial Networks (GAN). In this work, we employ TimeGAN, a GAN model focused on the generation of multivariate time series traces. We finally show how the proposed framework provides us with a set of metrics consistent with the observable behaviour and numerical insights on the quality of the generated data centre traces, which are hard to acquire otherwise.
On the design of an advanced business rule engine
(John Wiley & Sons, 2022) Jiménez Aguirre, Patricia; Corchuelo Gil, Rafael; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
Business rules govern how well-managed companies perform every day. They are expected to be written in natural language because they are devised by business people. That makes it difficult to translate them automatically into executable rules that can be integrated into typical information systems. Our industrial and academic research concludes that the current tools have a number of problems, namely: many of them do not pay any attention to the SBVR standard; some of them are not multi- language; most of them cannot achieve perfect parsing accuracy; some of them are not domain agnostic; some of them use proprietary technologies; some of them do not produce executable rules; and none supports exploratory what-if analyses natively. In this article, we present Tier-Rules, which is a system that overcomes the previous problems.We also report on an industrial case study that helps illustrate it in practice.
Special issue on advanced practices in web engineering 2021
(Rinton Press, 2021) Olivero González, Miguel Ángel; González Enríquez, José; Jiménez Ramírez, Andrés; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
Novelty in Web Engineering arises when this area is jointly applied with other emerging and innovative ones. The transference of Web Engineering research results to industrial applications is quick due to the extensive domains of applications that this area of knowledge has. Each year there are more and more users appealing for it. Consequently, the users’ expectations and demands on this area are higher and higher. Thus, the ability of researchers and developers to adopt advanced practices and adapt to innovative approaches within Web Engineering has a tremendous and direct impact on society. This special issue contains a selection of advanced practices on Web Engineering, according to their relevance and novelty. Six studies have been considered as highly relevant for the convenient development of actual webbased applications. Such selection has been organized into three main areas based on the focus of each study. These areas are: Software Development, Applied Technologies, and Linguistic Approaches.
Development of a liver graft assessment expert machine-learning system: when the artificial intelligence helps liver transplant surgeons
(Frontiers Media, 2024) Pontes Balanza, Beatriz; Castillo Tuñón, Juan M.; Mateos García, Daniel; Padillo Ruiz, Francisco Javier; Riquelme Santos, José Cristóbal; Álamo Martínez, José María; Bernal Bellido, Carmen; Suárez Artacho, Gonzalo; Cepeda Franco, Carmen; Gómez Bravo, Miguel Ángel; Marín Gómez, Luis Miguel; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; Universidad de Sevilla. Departamento de Cirugía
The complex process of liver graft assessment is one point for improvement in liver transplantation. The main objective of this study is to develop a tool that supports the surgeon who is responsible for liver donation in the decisionmaking processwhether to accept a graft or not using the initial variables available to it. Material andmethod: Liver graft samples candidate for liver transplantation after donor brain death were studied. All of them were evaluated “in situ” for transplantation, and those discarded after the “in situ” evaluation were considered as no transplantable liver grafts, while those grafts transplanted after “in situ” evaluation were considered as transplantable liver grafts. First, a single-center, retrospective and cohort study identifying the risk factors associated with the no transplantable group was performed. Then, a prediction model decision support system based on machine learning, and using a tree ensemble boosting classifier that is capable of helping to decide whether to accept or decline a donor liver graft, was developed. Results: A total of 350 liver grafts that were evaluated for liver transplantation were studied. Steatosis was the most frequent reason for classifying grafts as no transplantable, and the main risk factors identified in the univariant study were age, dyslipidemia, personal medical history, personal surgical history, bilirubinemia, and the result of previous liver ultrasound (p < 0.05). When studying the developed model, we observe that the best performance reordering in terms of accuracy corresponds to 76.29% with an area under the curve of 0.79. Furthermore, the model provides a classification together with a confidence index of reliability, for most cases in our data, with the probability of success in the prediction being above 0.85. Conclusion: The tool presented in this study obtains a high accuracy in predicting whether a liver graft will be transplanted or deemed non-transplantable based on the initial variables assigned to it. The inherent capacity for improvement in the system causes the rate of correct predictions to increase as new data are entered. Therefore, we believe it is a tool that can help optimize the graft pool for liver transplantation.
UVLHub: A feature model data repository using UVL and open science principles
(Elsevier, 2024-10) Romero Organvidez, David; Galindo Duarte, José Ángel; Sundermann, Chico; Horcas Aguilera, José Miguel; Benavides Cuevas, David Felipe; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
Feature models are the de facto standard for modelling variabilities and commonalities in features and relationships in software product lines. They are the base artefacts in many engineering activities, such as product configuration, derivation, or testing. Concrete models in different domains exist; however, many are in private or sparse repositories or belong to discontinued projects. The dispersion of knowledge of feature models hinders the study and reuse of these artefacts in different studies. The Universal Variability Language (UVL) is a community effort textual feature model language that promotes a common way of serializing feature models independently of concrete tools. Open science principles promote transparency, accessibility, and collaboration in scientific research. Although some attempts exist to promote feature model sharing, the existing solutions lack open science principles by design. In addition, existing and public feature models are described using formats not always supported by current tools. This paper presents UVLHub, a repository of feature models in UVL format. UVLHub provides a front end that facilitates the search, upload, storage, and management of feature model datasets, improving the capabilities of discontinued proposals. Furthermore, the tool communicates with Zenodo – one of the most well-known open science repositories – providing a permanent save of datasets and following open science principles. UVLHub includes existing datasets and is readily available to include new data and functionalities in the future. It is maintained by three active universities in variability modelling.
PDSS: A pharmacological decision support system for diabetics patients with COVID-19
(IO Press, 2023) Amaya-Rodríguez, Isabel; Larburu, Nekane; Martinez-Herrera, María Rollán; Rebescher, Kristin; Macia, Iván; Armengol de la Hoz, Miguel Ángel; Rubio Escudero, Cristina; Garín-Muga, Alba; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
With the advent of SARS-CoV-2, several studies have shown that there is a higher mortality rate in patients with diabetes and, in some cases, it is one of the side effects of overcoming the disease. However, there is no clinical decision support tool or specific treatment protocols for these patients. To tackle this issue, in this paper we present a Pharmacological Decision Support System (PDSS) providing intelligent decision support for COVID-19 diabetic patient treatment selection, based on an analysis of risk factors with data from electronic medical records using Cox regression. The goal of the system is to create real world evidence including the ability to continuously learn to improve clinical practice and outcomes of diabetic patients with COVID-19.
Introducing Joint Modeling Techniques for Personalized Predictions in Celiac Disease
(Elsevier INC, 2022) Serrano, Lidia; Rodriguez-Herrera, Alfonso; Serrat, Carles; Rubio Escudero, Cristina; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
If following a strict gluten-free diet in newly Celiac Disease (CD) antibodies titers are not declining, it may mean an active disease ongoing. As per guidelines additional invasive and costly tests use to be requested then. Disease progression, however, differs among patients, changing dynamically over time for the same individual. Current followup guidelines are in fashion of “one size fits all” and do not consider individual variations in the antibodies profile. We aim to tailored follow up interventions by identification of personalized predictive profiles.
Semi-real-time decision tree ensemble algorithms for very short-term solar irradiance forecasting
(Elsevier, 2024) Sánchez López, José Enrique; Solís García, Javier; Riquelme Santos, José Cristóbal; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; MCIU/AEI/10.13039/501100011033
Industrial activities are transitioning towards decarbonization, focusing on renewable energy sources, particularly photovoltaic solar energy. However, the inherent high variability of photovoltaic energy poses challenges. Some of them can be partially addressed by predicting electricity production, which in the case of photovoltaic solar energy is heavily based on solar irradiance prediction. Although extensive research has been conducted in this field, there is a noticeable gap in research regarding very short-term (intra-minute) forecasting under high-variability scenarios. In this proposal, real data from a photovoltaic solar plant in Alderville (Canada) were used to predict irradiance with a horizon of 15 and 30 s. The objective is to make this prediction in near-real time. To achieve this, we propose the use of machine learning algorithms based on decision tree ensembles, due to their low computational training cost and known effectiveness. On the other hand, we propose preprocessing the data through a temporal and spatial correlation analysis between measurements from different sensors. Feature selection analysis allows us to determine the direction of the wind and consequently identify the most relevant panels for model training. This preprocessing enhances the model retraining without the need for external information such as sky images or wind speed and direction on days with highly variable cloud cover. The presented methodology offers promising results with significantly reduced training times, demonstrating the suitability of this semi-online training approach for highly variable time series forecasting.
Análisis de la tasa de abandono en un Centro con varios Grados en Ingeniería Informática
(2017) Ruiz Cortés, David; Gómez Rodríguez, Francisco de Asís; Ruiz Reina, José Luis; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; Universidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadores; Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial
En este trabajo se muestra el análisis realizado del impacto que sobre la tasa de abandono tiene el cambio de estudios entre los tres Grados en Ingeniería Informática que se imparten en un Centro concreto. Dicho análisis ha sido llevado a cabo por el Equipo de Dirección del Centro a instancia de los informes realizados tras las visitas para la renovación de la acreditación de dichos títulos. Las principales conclusiones a las que hemos llegado son: i) el cambio de estudios entre Grados en Informática siempre tiene un efecto negativo sobre la tasa de abandono, oscilando este entre el 3% y el 20 %; ii) dicho cambio de estudios puede responder a cuestiones académicas en algunos casos, pero también se apuntan cuestiones económicas por el ahorro que puede llegar a suponer; iii) aproximadamente un tercio de nuestros estudiantes abandona los estudios en Ingeniería Informática; iv) la tasa de abandono a lo largo de los últimos 5 años se ha mantenido acorde con lo establecido en las memorias de verificación y conforme a la media nacional en la rama de conocimiento de Ingeniería y Arquitectura; v) los sistemas de indicadores definidos por los distintos sistemas de garantía de calidad de los Títulos en ocasiones no son homogéneos, lo que dificulta realizar cualquier tipo de análisis.
Variability management and software product line knowledge in software companies
(Elsevier, 2024) Gutiérrez Fernández, Antonio Manuel; Chacón Luna, Ana Eva; Benavides Cuevas, David Felipe; Fuentes, Lidia; Rabiser, Rick; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
Software product line engineering aims to systematically generate similar products or services within a given domain to reduce cost and time to market while increasing reuse. Various studies recognize the success of product line engineering in different domains. Software variability have increased over the years in many different domains such as mobile applications, cyber–physical systems or car control systems to just mention a few. However, software product line engineering is not as widely adopted as other software development technologies. In this paper, we present an empirical study conducted through a survey distributed to many software development companies. Our goal is to understand their need of software variability management and the level of knowledge the companies have regarding software product line engineering. The survey was answered by 127 participants from more than a hundred of different software development companies. Our study reveals that most of companies manage a catalog of similar products in a way or another (e.g. cloneand- own, common modules that are statically imported,etc.), they mostly document the features of products using text or spreed sheet based documents and more than 66% of companies identify a base product from which they derive other similar products. We also found a correlation between the lack of Software Product Line (SPL) knowledge and the absence of reuse practices. Notably, this is the first study that explore software variability needs regardless of a company’s prior knowledge of SPL. The results encourages further research to understand the reason for the limited knowledge and application of software product line engineering practices, despite the growing demand of variability management.
Testing of highly configurable cyber-physical systems-Results from a two-phase multiple case study
(Elsevier, 2023) Fischer, Stephan; Gutiérrez Fernández, Antonio Manuel; Gutiérrez Fernández, Antonio Manuel; Rabiser, R.; Ramler, R.; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
Cyber–physical systems are commonly highly configurable. Testing such systems is particularly challenging because they comprise numerous heterogeneous components that can be configured and combined in different ways. Despite a plethora of work investigating software testing in general and software product line testing in particular, variability in tests and how it is applied in industry with cyber–physical systems is not well understood. In this paper, we report on a multiple case study with four companies maintaining highly configurable cyber–physical systems focusing on their testing practices, with a particular focus on variability. Based on the results of the multiple case study, we conducted an interactive survey with experienced engineers from eight companies, including the initial four. We reflect on the lessons learned from the multiple case study. We conclude that experiencebased selection of configurations for testing is currently predominant. We learned that variability modeling techniques and tools are not utilized and the dependencies between configuration options are only partially modeled at best using custom artifacts such as spreadsheets or configuration files. Another finding is that companies have the need and desire to cover more configuration combinations by automated tests. Our findings raise many questions interesting to the scientific community and motivating future research.
Dynamic Product Configuration User Interface: A Vision Motivated by the Cyber-Physical Production Systems Domain
(Association for Computing Machinery, 2023) Fadhlillah, Hafiyyan Sayyid; Feichtinger, Kevin; Gutiérrez Fernández, Antonio Manuel; Rabiser, Rick; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
Cyber-Physical Production Systems (CPPSs) are large-scale industrial systems in which hardware and software are deeply intertwined. CPPS software has to be highly variable to support frequently changing customer and hardware requirements. Managing the overall variability of such large-scale industrial software is challenging. Knowledge from diverse engineering disciplines, e.g., mechatronics, process, electrical, and control software engineering, is required to support product configuration. These disciplines use diverse artifacts and tools. It is infeasible to have one common variability model or one common configuration user interface representing variability knowledge from diverse disciplines in CPPSs. Therefore, in this paper, we propose the idea of a dynamic product configuration user interface, which decouples the product configuration process from the user interface based on an existing multidisciplinary variability management approach for CPPSs. We describe the next steps toward implementing and evaluating our idea.

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