115 research outputs found

    The state of adoption and the challenges of systematic variability management in industry

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    Handling large-scale software variability is still a challenge for many organizations. After decades of research on variability management concepts, many industrial organizations have introduced techniques known from research, but still lament that pure textbook approaches are not applicable or efficient. For instance, software product line engineering—an approach to systematically develop portfolios of products—is difficult to adopt given the high upfront investments; and even when adopted, organizations are challenged by evolving their complex product lines. Consequently, the research community now mainly focuses on re-engineering and evolution techniques for product lines; yet, understanding the current state of adoption and the industrial challenges for organizations is necessary to conceive effective techniques. In this multiple-case study, we analyze the current adoption of variability management techniques in twelve medium- to large-scale industrial cases in domains such as automotive, aerospace or railway systems. We identify the current state of variability management, emphasizing the techniques and concepts they adopted. We elicit the needs and challenges expressed for these cases, triangulated with results from a literature review. We believe our results help to understand the current state of adoption and shed light on gaps to address in industrial practice.This work is supported by Vinnova Sweden, Fond Unique Interminist´eriel (FUI) France, and the Swedish Research Council. Open access funding provided by University of Gothenbur

    Design of a Machine Learning-based Approach for Fragment Retrieval on Models

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    [ES] El aprendizaje automático (ML por sus siglas en inglés) es conocido como la rama de la inteligencia artificial que reúne algoritmos estadísticos, probabilísticos y de optimización, que aprenden empíricamente. ML puede aprovechar el conocimiento y la experiencia que se han generado durante años en las empresas para realizar automáticamente diferentes procesos. Por lo tanto, ML se ha aplicado a diversas áreas de investigación, que estudian desde la medicina hasta la ingeniería del software. De hecho, en el campo de la ingeniería del software, el mantenimiento y la evolución de un sistema abarca hasta un 80% de la vida útil del sistema. Las empresas, que se han dedicado al desarrollo de sistemas software durante muchos años, han acumulado grandes cantidades de conocimiento y experiencia. Por lo tanto, ML resulta una solución atractiva para reducir sus costos de mantenimiento aprovechando los recursos acumulados. Específicamente, la Recuperación de Enlaces de Trazabilidad, la Localización de Errores y la Ubicación de Características se encuentran entre las tareas más comunes y relevantes para realizar el mantenimiento de productos software. Para abordar estas tareas, los investigadores han propuesto diferentes enfoques. Sin embargo, la mayoría de las investigaciones se centran en métodos tradicionales, como la indexación semántica latente, que no explota los recursos recopilados. Además, la mayoría de las investigaciones se enfocan en el código, descuidando otros artefactos de software como son los modelos. En esta tesis, presentamos un enfoque basado en ML para la recuperación de fragmentos en modelos (FRAME). El objetivo de este enfoque es recuperar el fragmento del modelo que realiza mejor una consulta específica. Esto permite a los ingenieros recuperar el fragmento que necesita ser trazado, reparado o ubicado para el mantenimiento del software. Específicamente, FRAME combina la computación evolutiva y las técnicas ML. En FRAME, un algoritmo evolutivo es guiado por ML para extraer de manera eficaz distintos fragmentos de un modelo. Estos fragmentos son posteriormente evaluados mediante técnicas ML. Para aprender a evaluarlos, las técnicas ML aprovechan el conocimiento (fragmentos recuperados de modelos) y la experiencia que las empresas han generado durante años. Basándose en lo aprendido, las técnicas ML determinan qué fragmento del modelo realiza mejor una consulta. Sin embargo, la mayoría de las técnicas ML no pueden entender los fragmentos de los modelos. Por lo tanto, antes de aplicar las técnicas ML, el enfoque propuesto codifica los fragmentos a través de una codificación ontológica y evolutiva. En resumen, FRAME está diseñado para extraer fragmentos de un modelo, codificarlos y evaluar cuál realiza mejor una consulta específica. El enfoque ha sido evaluado a partir de un caso real proporcionado por nuestro socio industrial (CAF, un proveedor internacional de soluciones ferroviarias). Además, sus resultados han sido comparados con los resultados de los enfoques más comunes y recientes. Los resultados muestran que FRAME obtuvo los mejores resultados para la mayoría de los indicadores de rendimiento, proporcionando un valor medio de precisión igual a 59.91%, un valor medio de exhaustividad igual a 78.95%, una valor-F medio igual a 62.50% y un MCC (Coeficiente de Correlación Matthews) medio igual a 0.64. Aprovechando los fragmentos recuperados de los modelos, FRAME es menos sensible al conocimiento tácito y al desajuste de vocabulario que los enfoques basados en información semántica. Sin embargo, FRAME está limitado por la disponibilidad de fragmentos recuperados para llevar a cabo el aprendizaje automático. Esta tesis presenta una discusión más amplia de estos aspectos así como el análisis estadístico de los resultados, que evalúa la magnitud de la mejora en comparación con los otros enfoques.[CAT] L'aprenentatge automàtic (ML per les seues sigles en anglés) és conegut com la branca de la intel·ligència artificial que reuneix algorismes estadístics, probabilístics i d'optimització, que aprenen empíricament. ML pot aprofitar el coneixement i l'experiència que s'han generat durant anys en les empreses per a realitzar automàticament diferents processos. Per tant, ML s'ha aplicat a diverses àrees d'investigació, que estudien des de la medicina fins a l'enginyeria del programari. De fet, en el camp de l'enginyeria del programari, el manteniment i l'evolució d'un sistema abasta fins a un 80% de la vida útil del sistema. Les empreses, que s'han dedicat al desenvolupament de sistemes programari durant molts anys, han acumulat grans quantitats de coneixement i experiència. Per tant, ML resulta una solució atractiva per a reduir els seus costos de manteniment aprofitant els recursos acumulats. Específicament, la Recuperació d'Enllaços de Traçabilitat, la Localització d'Errors i la Ubicació de Característiques es troben entre les tasques més comunes i rellevants per a realitzar el manteniment de productes programari. Per a abordar aquestes tasques, els investigadors han proposat diferents enfocaments. No obstant això, la majoria de les investigacions se centren en mètodes tradicionals, com la indexació semàntica latent, que no explota els recursos recopilats. A més, la majoria de les investigacions s'enfoquen en el codi, descurant altres artefactes de programari com són els models. En aquesta tesi, presentem un enfocament basat en ML per a la recuperació de fragments en models (FRAME). L'objectiu d'aquest enfocament és recuperar el fragment del model que realitza millor una consulta específica. Això permet als enginyers recuperar el fragment que necessita ser traçat, reparat o situat per al manteniment del programari. Específicament, FRAME combina la computació evolutiva i les tècniques ML. En FRAME, un algorisme evolutiu és guiat per ML per a extraure de manera eficaç diferents fragments d'un model. Aquests fragments són posteriorment avaluats mitjançant tècniques ML. Per a aprendre a avaluar-los, les tècniques ML aprofiten el coneixement (fragments recuperats de models) i l'experiència que les empreses han generat durant anys. Basant-se en l'aprés, les tècniques ML determinen quin fragment del model realitza millor una consulta. No obstant això, la majoria de les tècniques ML no poden entendre els fragments dels models. Per tant, abans d'aplicar les tècniques ML, l'enfocament proposat codifica els fragments a través d'una codificació ontològica i evolutiva. En resum, FRAME està dissenyat per a extraure fragments d'un model, codificar-los i avaluar quin realitza millor una consulta específica. L'enfocament ha sigut avaluat a partir d'un cas real proporcionat pel nostre soci industrial (CAF, un proveïdor internacional de solucions ferroviàries). A més, els seus resultats han sigut comparats amb els resultats dels enfocaments més comuns i recents. Els resultats mostren que FRAME va obtindre els millors resultats per a la majoria dels indicadors de rendiment, proporcionant un valor mitjà de precisió igual a 59.91%, un valor mitjà d'exhaustivitat igual a 78.95%, una valor-F mig igual a 62.50% i un MCC (Coeficient de Correlació Matthews) mig igual a 0.64. Aprofitant els fragments recuperats dels models, FRAME és menys sensible al coneixement tàcit i al desajustament de vocabulari que els enfocaments basats en informació semàntica. No obstant això, FRAME està limitat per la disponibilitat de fragments recuperats per a dur a terme l'aprenentatge automàtic. Aquesta tesi presenta una discussió més àmplia d'aquests aspectes així com l'anàlisi estadística dels resultats, que avalua la magnitud de la millora en comparació amb els altres enfocaments.[EN] Machine Learning (ML) is known as the branch of artificial intelligence that gathers statistical, probabilistic, and optimization algorithms, which learn empirically. ML can exploit the knowledge and the experience that have been generated for years to automatically perform different processes. Therefore, ML has been applied to a wide range of research areas, from medicine to software engineering. In fact, in software engineering field, up to an 80% of a system's lifetime is spent on the maintenance and evolution of the system. The companies, that have been developing these software systems for a long time, have gathered a huge amount of knowledge and experience. Therefore, ML is an attractive solution to reduce their maintenance costs exploiting the gathered resources. Specifically, Traceability Link Recovery, Bug Localization, and Feature Location are amongst the most common and relevant tasks when maintaining software products. To tackle these tasks, researchers have proposed a number of approaches. However, most research focus on traditional methods, such as Latent Semantic Indexing, which does not exploit the gathered resources. Moreover, most research targets code, neglecting other software artifacts such as models. In this dissertation, we present an ML-based approach for fragment retrieval on models (FRAME). The goal of this approach is to retrieve the model fragment which better realizes a specific query in a model. This allows engineers to retrieve the model fragment, which must be traced, fixed, or located for software maintenance. Specifically, the FRAME approach combines evolutionary computation and ML techniques. In the FRAME approach, an evolutionary algorithm is guided by ML to effectively extract model fragments from a model. These model fragments are then assessed through ML techniques. To learn how to assess them, ML techniques takes advantage of the companies' knowledge (retrieved model fragments) and experience. Then, based on what was learned, ML techniques determine which model fragment better realizes a query. However, model fragments are not understandable for most ML techniques. Therefore, the proposed approach encodes the model fragments through an ontological evolutionary encoding. In short, the FRAME approach is designed to extract model fragments, encode them, and assess which one better realizes a specific query. The approach has been evaluated in our industrial partner (CAF, an international provider of railway solutions) and compared to the most common and recent approaches. The results show that the FRAME approach achieved the best results for most performance indicators, providing a mean precision value of 59.91%, a recall value of 78.95%, a combined F-measure of 62.50%, and a MCC (Matthews correlation coefficient) value of 0.64. Leveraging retrieved model fragments, the FRAME approach is less sensitive to tacit knowledge and vocabulary mismatch than the approaches based on semantic information. However, the approach is limited by the availability of the retrieved model fragments to perform the learning. These aspects are further discussed, after the statistical analysis of the results, which assesses the magnitude of the improvement in comparison to the other approaches.Marcén Terraza, AC. (2020). Design of a Machine Learning-based Approach for Fragment Retrieval on Models [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/158617TESI

    Definition of Descriptive and Diagnostic Measurements for Model Fragment Retrieval

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    Tesis por compendio[ES] Hoy en día, el software existe en casi todo. Las empresas a menudo desarrollan y mantienen colecciones de sistemas de software personalizados que comparten algunas características entre ellos, pero que también tienen otras características particulares. Conforme el número de características y el número de variantes de un producto crece, el mantenimiento del software se vuelve cada vez más complejo. Para hacer frente a esta situación la Comunidad de Ingeniería del Software basada en Modelos está abordando una actividad clave: la Localización de Fragmentos de Modelo. Esta actividad consiste en la identificación de elementos del modelo que son relevantes para un requisito, una característica o un bug. Durante los últimos años se han propuesto muchos enfoques para abordar la identificación de los elementos del modelo que corresponden a una funcionalidad en particular. Sin embargo, existe una carencia a la hora de cómo se reportan las medidas del espacio de búsqueda, así como las medidas de la solución a encontrar. El objetivo de nuestra tesis radica en proporcionar a la comunidad dedicada a la actividad de localización de fragmentos de modelo una serie de medidas (tamaño, volumen, densidad, multiplicidad y dispersión) para reportar los problemas de localización de fragmentos de modelo. El uso de estas novedosas medidas ayuda a los investigadores durante la creación de nuevos enfoques, así como la mejora de aquellos enfoques ya existentes. Mediante el uso de dos casos de estudio reales e industriales, esta tesis pone en valor la importancia de estas medidas para comparar resultados de diferentes enfoques de una manera precisa. Los resultados de este trabajo han sido redactados y publicados en foros, conferencias y revistas especializadas en los temas y contexto de la investigación. Esta tesis se presenta como un compendio de artículos acorde a la regulación de la Universitat Politècnica de València. Este documento de tesis presenta los temas, el contexto y los objetivos de la investigación. Presenta las publicaciones académicas que se han publicado como resultado del trabajo y luego analiza los resultados de la investigación.[CA] Hui en dia, el programari existix en quasi tot. Les empreses sovint desenrotllen i mantenen col·leccions de sistemes de programari personalitzats que compartixen algunes característiques entre ells, però que també tenen altres característiques particulars. Conforme el nombre de característiques i el nombre de variants d'un producte creix, el manteniment del programari es torna cada vegada més complex. Per a fer front a esta situació la Comunitat d'Enginyeria del Programari basada en Models està abordant una activitat clau: la Localització de Fragments de Model. Esta activitat consistix en la identificació d'elements del model que són rellevants per a un requisit, una característica o un bug. Durant els últims anys s'han proposat molts enfocaments per a abordar la identificació dels elements del model que corresponen a una funcionalitat en particular. No obstant això, hi ha una carència a l'hora de com es reporten les mesures de l'espai de busca, així com les mesures de la solució a trobar. L'objectiu de la nostra tesi radica a proporcionar a la comunitat dedicada a l'activitat de localització de fragments de model una sèrie de mesures (grandària, volum, densitat, multiplicitat i dispersió) per a reportar els problemes de localització de fragments de model. L'ús d'estes noves mesures ajuda als investigadors durant la creació de nous enfocaments, així com la millora d'aquells enfocaments ja existents. Per mitjà de l'ús de dos casos d'estudi reals i industrials, esta tesi posa en valor la importància d'estes mesures per a comparar resultats de diferents enfocaments d'una manera precisa. Els resultats d'este treball han sigut redactats i publicats en fòrums, conferències i revistes especialitzades en els temes i context de la investigació. Esta tesi es presenta com un compendi d'articles d'acord amb la regulació de la Universitat Politècnica de València. Este document de tesi presenta els temes, el context i els objectius de la investigació. Presenta les publicacions acadèmiques que s'han publicat com resultat del treball i després analitza els resultats de la investigació.[EN] Nowadays, software exists in almost everything. Companies often develop and maintain a collection of custom-tailored software systems that share some common features but also support customer-specific ones. As the number of features and the number of product variants grows, software maintenance is becoming more and more complex. To keep pace with this situation, Model-Based Software Engineering Community is addressing a key-activity: Model Fragment Location (MFL). MFL aims at identifying model elements that are relevant to a requirement, feature, or bug. Many MFL approaches have been introduced in the last few years to address the identification of the model elements that correspond to a specific functionality. However, there is a lack of detail when the measurements about the search space (models) and the measurements about the solution to be found (model fragment) are reported. The goal of this thesis is to provide insights to MFL Research Community of how to improve the report of location problems. We propose using five measurements (size, volume, density, multiplicity, and dispersion) to report the location problems during MFL. The usage of these novel measurements support researchers during the creation of new MFL approaches and during the improvement of those existing ones. Using two different case studies, both real and industrial, we emphasize the importance of these measurements in order to compare results in a deeply way. The results of the research have been redacted and published in forums, conferences, and journals specialized in the topics and context of the research. This thesis is presented as compendium of articles according the regulations in Universitat Politècnica de València. This thesis document introduces the topics, context, and objectives of the research, presents the academic publications that have been published as a result of the work, and then discusses the outcomes of the investigation.Ballarin Naya, M. (2021). Definition of Descriptive and Diagnostic Measurements for Model Fragment Retrieval [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/171604TESISCompendi

    A decision-making framework to reduce the risk of collisions between ships and whales

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    Ship strikes are one of the main human-induced threats to whale survival. A variety of measures have been used or proposed to reduce collisions and subsequent mortality of whales. These include operational measures, such as mandatory speed reduction, or technical ones, such as detection tools. There is, however, a lack of a systematic approach to assessing the various measures that can mitigate the risk of ship collisions with whales. In this paper, a holistic approach is proposed to evaluate mitigation measures based on a risk assessment framework that has been adopted by the International Maritime Organization (IMO), namely the Formal Safety Assessment (FSA). Formal Safety Assessment (FSA) is “a rational and systematic process for assessing the risk related to maritime safety and the protection of the marine environment and for evaluating the costs and benefits of IMO's options for reducing these risks”. The paper conceptualizes the use of a systematic risk assessment methodology, namely the FSA, to assess measures to reduce the risk of collisions between ships and whales

    Cuban energy system development – Technological challenges and possibilities

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    This eBook is a unique scientific journey to the changing frontiers of energy transition in Cuba focusing on technological challenges of the Cuban energy transition. The focus of this milestone publication is on technological aspects of energy transition in Cuba. Green energy transition with renewable energy sources requires the ability to identify opportunities across industries and services and apply the right technologies and tools to achieve more sustainable energy production systems. The eBook is covering a large diversity of Caribbean country´s experiences of new green technological solutions and applications. It includes various technology assessments of energy systems and technological foresight analyses with a special focus on Cuba

    Relationships Between Middle School Students\u27 Adaptive Reasoning When Creating Learner-Generated Drawings and Partner Talk During Inquiry-Based Mathematical Tasks

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    Adaptive reasoning is one of five components students use to develop mathematical expertise and become mathematically proficient. When students adapt their reasoning they are logically thinking about the mathematical relationships between concepts and adapting their thinking to solve problems. Three Act Math Tasks are popular math problems used in schools in which students engage in adaptive reasoning. These types of problems are beneficial to students because they engage students in inquiry-based learning, a kind of learning where students work to pose questions, interpret data, design ways to solve the problem and present their solutions. Little is known about how students adapt their reasoning as they partake in these types of tasks. The objective of this study is to better understand what adaptive reasoning strategies seventh graders used and how they used these strategies when engaged in inquiry-based mathematical tasks. To accomplish this, the study observed 18 seventh grade students as they worked through three mathematical tasks. The researcher observed student discussions and their drawings to see what adaptive reasoning strategies were being used by students and how the strategies were used throughout different stages of the tasks. In this way a more complete picture of how students adapted their reasoning was obtained. The researcher analyzed student use of six different adaptive reasoning indicators, including: 1) relationships and connections, 2) justifications, 3) alternates pursued, 4) prior knowledge, 5) legitimacy determined, and 6) pattern recognition. Results indicate that students used all six adaptive reasoning strategies. Students primarily adapted their reasoning by finding relationships and connections and making justifications. Additionally, each student demonstrated a unique pattern of adaptive reasoning strategies which was mediated by their partner. Use of the other four indicators, alternates pursued, prior knowledge, legitimacy determined and pattern recognition were used in conjunction with the two primary indicators. Additionally, different patterns of use were identified within the separate modalities of student drawings and discussions. This study is beneficial because it helps teachers and researchers better understand what adaptive reasoning strategies students are utilizing and the relationship between these strategies in a classroom setting. This affords teachers and researchers opportunities to develop better learning experiences and understand how students reason in light of mathematical proficiency

    BEYOND ALL LIMITS : Procedings on International Conference on Sustainability in Architecture, Planning, and Design : 11-12, 13 May 2022

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    [Italiano]: Il volume raccoglie gli atti della seconda edizione del convegno “BEYOND ALL LIMITS. International Conference on Sustainability in Architecture, Planning, and Design”, tenutosi nei giorni 11 e 12 maggio 2022, presso il Complesso del Belvedere di San Leucio, sede di Officina Vanvitelli. Il convegno è stato promosso e organizzato dal Dipartimento di Architettura e Disegno Industriale dell'Università degli Studi della Campania “Luigi Vanvitelli”, in partnership con la Faculty of Architecture della Çankaya University di Ankara e la Faculty of Engineering della University of Strathclyde di Glasgow. L’obiettivo principale di questo convegno scientifico e multidisciplinare, che ha interessato i campi dell'architettura, della pianificazione e del design, è stato quello di affrontare il tema della sostenibilità all’interno dell'attuale dibattito internazionale scaturito dal New European Bauhaus (NEB)./[English]: This volume collects the Proceedings of the second edition of the conference “BEYOND ALL LIMITS. International Conference on Sustainability in Architecture, Planning, and Design”, held on May 11 and 12, 2022, at the San Leucio Belvedere Complex, home of Officina Vanvitelli. The conference was sponsored and organized by the Department of Architecture and Industrial Design of the University of Campania “Luigi Vanvitelli”, in partnership with the Faculty of Architecture of Çankaya University in Ankara and the Faculty of Engineering of the University of Strathclyde in Glasgow. The main objective of this scientific and multidisciplinary conference, which covered the fields of architecture, planning and design, was to address the issue of sustainability within the current international debate that has arisen from the New European Bauhaus (NEB)

    The Palgrave Handbook of International Energy Economics

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    This open access handbook is distinguished by its emphasis on international energy, rather than domestic energy policies or international geopolitic aspects. Addressing key topics such as energy production and distribution, renewables and corporate energy structures, alongside global energy trends, regional case studies and emerging areas such as the digitalization of energy and energy transition, this handbook provides a major new contribution to the field of international energy economics. Written by academics, practitioners and policy-makers, this handbook is a valuable and timely addition to the literature on international energy economics. This book was published open access with the support of Eni

    Digital Business Models in Sport

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    Digital technologies are having a profound impact on sport business, opening up new opportunities to generate income and value. This book explores the phenomenon of digitization in sport management, with a particular focus on business models and how they are being transformed in this new digital era. The book explains how business models describe and underpin contemporary sport business, and how flexibility is the key to unlocking value in an era of rapid technological change. It presents case studies of the impact of digitization on sport organizations, in both amateur and professional contexts, including cutting-edge topics such as the business of football, sponsorship communication, athlete engagement, micropayments and wearable devices. The final chapter summarizes current knowledge on digital business models and looks ahead at possible future directions for sport business in the digital era. This is fascinating reading for any advanced student, researcher or practitioner working in sport management who wants to better understand the challenges and opportunities presented by digital technology for the sport industry
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