Ontological support for the evolution of future services oriented architectures

Services Oriented Architectures (SOA) have emerged as a useful framework for developing interoperable, large-scale systems, typically implemented using the Web Services (WS) standards. However, the maintenance and evolution of SOA systems present many challenges. SmartLife applications are intelligent user-centered systems and a special class of SOA systems that present even greater challenges for a software maintainer. Ontologies and ontological modeling can be used to support the evolution of SOA systems. This paper describes the development of a SOA evolution ontology and its use to develop an ontological model of a SOA system. The ontology is based on a standard SOA ontology. The ontological model can be used to provide semantic and visual support for software maintainers during routine maintenance tasks. We discuss a case study to illustrate this approach, as well as the strengths and limitations

How do we acquire understanding of conceptual models?

In organizations, conceptual models are used for understanding the domain concepts. Such models are crucial in analysis and development of information systems. An important factor of using the conceptual models is how quickly analysts are able to learn the domain concepts as depicted in the models. Using a laboratory experiment, this research used eye tracking technique to capture the speed of acquisition of understanding conceptual models. Two sets of conceptual models were used in this study- one theory based (REA pattern) and the other non-theory based (non REA pattern). It was found that the rate of learning of the domain concepts was faster with theory based models than with non-theory based models. However, users of the non-theory based model were able to catch up with the learning of the model concepts after being repeatedly exposed to the model

BIM semantic-enrichment for built heritage representation

In the built heritage context, BIM has shown difficulties in representing and managing the large and complex knowledge related to non-geometrical aspects of the heritage. Within this scope, this paper focuses on a domain-specific semantic-enrichment of BIM methodology, aimed at fulfilling semantic representation requirements of built heritage through Semantic Web technologies. To develop this semantic-enriched BIM approach, this research relies on the integration of a BIM environment with a knowledge base created through information ontologies. The result is knowledge base system - and a prototypal platform - that enhances semantic representation capabilities of BIM application to architectural heritage processes. It solves the issue of knowledge formalization in cultural heritage informative models, favouring a deeper comprehension and interpretation of all the building aspects. Its open structure allows future research to customize, scale and adapt the knowledge base different typologies of artefacts and heritage activities

Robot Consciousness: Physics and Metaphysics Here and Abroad

Interest has been renewed in the study of consciousness, both theoretical and applied, following developments in 20th and early 21st-century logic, metamathematics, computer science, and the brain sciences. In this evolving narrative, I explore several theoretical questions about the types of artificial intelligence and offer several conjectures about how they affect possible future developments in this exceptionally transformative field of research. I also address the practical significance of the advances in artificial intelligence in view of the cautions issued by prominent scientists, politicians, and ethicists about the possible dangers of such sufficiently advanced general intelligence, including by implication the search for extraterrestrial intelligence

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

[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

On the Effect of Semantically Enriched Context Models on Software Modularization

Many of the existing approaches for program comprehension rely on the linguistic information found in source code, such as identifier names and comments. Semantic clustering is one such technique for modularization of the system that relies on the informal semantics of the program, encoded in the vocabulary used in the source code. Treating the source code as a collection of tokens loses the semantic information embedded within the identifiers. We try to overcome this problem by introducing context models for source code identifiers to obtain a semantic kernel, which can be used for both deriving the topics that run through the system as well as their clustering. In the first model, we abstract an identifier to its type representation and build on this notion of context to construct contextual vector representation of the source code. The second notion of context is defined based on the flow of data between identifiers to represent a module as a dependency graph where the nodes correspond to identifiers and the edges represent the data dependencies between pairs of identifiers. We have applied our approach to 10 medium-sized open source Java projects, and show that by introducing contexts for identifiers, the quality of the modularization of the software systems is improved. Both of the context models give results that are superior to the plain vector representation of documents. In some cases, the authoritativeness of decompositions is improved by 67%. Furthermore, a more detailed evaluation of our approach on JEdit, an open source editor, demonstrates that inferred topics through performing topic analysis on the contextual representations are more meaningful compared to the plain representation of the documents. The proposed approach in introducing a context model for source code identifiers paves the way for building tools that support developers in program comprehension tasks such as application and domain concept location, software modularization and topic analysis

Dealing with Complexity in Information Systems Modeling: Development and Empirical Validation of a Method for Representing Large Data Models

One of the most serious practical and theoretical limitations of the entity-relationship (E-R) model is its inability to cope with complexity. Once E-R models exceed a certain threshold of size, they become difficult to understand, document and maintain. This paper describes the development and empirical validation of a method for representing large E-R models called leveled data modeling (LDM). A combination of research methods were used to validate the method. Action research was first used to test and refine the method in a real-world setting. Eight action research studies were conducted in eight different organizations. Once the method had become stable, two laboratory experiments were conducted to evaluate its effectiveness compared to the standard E-R model and methods previously proposed in the literature. Finally, a field experiment was conducted using experienced practitioners to evaluate the likelihood of the method being accepted in practice. The resulting method defines a general approach for managing complexity which could be applied to any information systems modeling technique. The research findings thus have general implications for developing more effective IS design techniques. Another contribution of the paper is that it illustrates a systematic, multi- method approach to empirically validating an IS design method

Ontology-based model abstraction

In recent years, there has been a growth in the use of reference conceptual models to capture information about complex and critical domains. However, as the complexity of domain increases, so does the size and complexity of the models that represent them. Over the years, different techniques for complexity management in large conceptual models have been developed. In particular, several authors have proposed different techniques for model abstraction. In this paper, we leverage on the ontologically well-founded semantics of the modeling language OntoUML to propose a novel approach for model abstraction in conceptual models. We provide a precise definition for a set of Graph-Rewriting rules that can automatically produce much-reduced versions of OntoUML models that concentrate the models’ information content around the ontologically essential types in that domain, i.e., the so-called Kinds. The approach has been implemented using a model-based editor and tested over a repository of OntoUML models