149 research outputs found

    Análisis de Enfoques de Model Based Testing para Pruebas Funcionales orientados a Aplicaciones Web

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    En los últimos años las aplicaciones web han ido incrementando en número y a la vez en complejidad debido a la incorporación de nueva tecnología. Esto ha repercutido en un aumento de complejidad de la fase de pruebas dentro del ciclo de vida del desarrollo de software, la cual nos permite asegurar la calidad del producto desarrollado. Esta fase representa un mayor costo y esfuerzo. Con otro tipo de aplicaciones no se le asignaba el tiempo ni esfuerzo necesario. Sin embargo, debido al impacto que puede tener una aplicación web mal probada durante la puesta en marcha de la aplicación, han surgido diversas investigaciones en técnicas para la simplificación de la fase de pruebas. Una de estas técnicas es model based testing, que mediante la representación del comportamiento esperado de la aplicación, genera automáticamente los casos de prueba, incluso permite la ejecución automática de los mismos y su evaluación. El presente trabajo presenta una revisión analítica de los enfoques en model based testing para aplicaciones web orientados a pruebas funcionales, identificando para ello los enfoques existentes dentro de este contexto y realizando un esquema de caracterización para el análisis de las principales características, herramientas y documentación disponible para la aplicación de los enfoques.Universidad de Sevilla. Master Universitario en Ingeniería y Tecnología del Softwar

    Generalization of the model to implementation mapping tool

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    Tese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 200

    Verifying Web Applications: From Business Level Specifications to Automated Model-Based Testing

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    One of reasons preventing a wider uptake of model-based testing in the industry is the difficulty which is encountered by developers when trying to think in terms of properties rather than linear specifications. A disparity has traditionally been perceived between the language spoken by customers who specify the system and the language required to construct models of that system. The dynamic nature of the specifications for commercial systems further aggravates this problem in that models would need to be rechecked after every specification change. In this paper, we propose an approach for converting specifications written in the commonly-used quasi-natural language Gherkin into models for use with a model-based testing tool. We have instantiated this approach using QuickCheck and demonstrate its applicability via a case study on the eHealth system, the national health portal for Maltese residents.Comment: In Proceedings MBT 2014, arXiv:1403.704

    Bayesian photometric redshifts with empirical training sets

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    We combine in a single framework the two complementary benefits of chi^2-template fits and empirical training sets used e.g. in neural nets: chi^2 is more reliable when its probability density functions (PDFs) are inspected for multiple peaks, while empirical training is more accurate when calibration and priors of query data and training set match. We present a chi^2-empirical method that derives PDFs from empirical models as a subclass of kernel regression methods, and apply it to the SDSS DR5 sample of >75,000 QSOs, which is full of ambiguities. Objects with single-peak PDFs show <1% outliers, rms redshift errors 2.5, these figures are 2x better. Outliers result purely from the discrete nature and limited size of the model, and rms errors are dominated by the instrinsic variety of object colours. PDFs classed as ambiguous provide accurate probabilities for alternative solutions and thus weights for using both solutions and avoiding needless outliers. E.g., the PDFs predict 78.0% of the stronger peaks to be correct, which is true for 77.9% of them. Redshift incompleteness is common in faint spectroscopic surveys and turns into a massive undetectable outlier risk above other performance limitations, but we can quantify residual outlier risks stemming from size and completeness of the model. We propose a matched chi^2-error scale for noisy data and show that it produces correct error estimates and redshift distributions accurate within Poisson errors. Our method can easily be applied to future large galaxy surveys, which will benefit from the reliability in ambiguity detection and residual risk quantification.Comment: accepted for publication in MNRA

    A tertiary study on model-based testing areas, tools and challenges: Preliminary results

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    Context: Model-based testing (MBT) is one of the most studied approaches by secondary studies in the area of software testing. A tertiary study that aggregates knowledge from secondary studies on MBT can be useful to both academia and industry. Objective: The goal of this study is to identify and characterize secondary studies in model-based testing, in terms of the areas, tools and challenges they have investigated. Method: We conducted a tertiary study in MBT. Our systematic mapping of secondary studies included 12 literature surveys and 10 systematic reviews over the period 1996–2016.Results: We found that the two most studied areas of MBT are UML models and Transition-based notations. We also found that only 5 studies compared and classified MBT tools. The main challenges and limitations found were related to the need for more empirical evidence that supports the selection of MBT approaches and tools. Conclusions: Not many systematic reviews on MBT were found, consequently some areas still lack secondary studies: test execution aspects, language types, model dynamics, and some model paradigms and generation methods. We thus encourage the MBT community to perform further systematic reviews and mapping studies, following known protocols and reporting procedures, in order to increase the quality and quantity of empirical studies in MBT.Universidad de Costa Rica/[834-B7-749]/UCR/Costa RicaUCR::Vicerrectoría de Docencia::Ingeniería::Facultad de Ingeniería::Escuela de Ciencias de la Computación e InformáticaUCR::Vicerrectoría de Investigación::Unidades de Investigación::Ingeniería::Centro de Investigaciones en Tecnologías de Información y Comunicación (CITIC

    Creation, refinement, and evaluation of conformational ensembles of proteins using the Torsional Network Model

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    Máster Universitario en Bioinformática y Biología ComputacionalOne of the main limitations of structural bioinformatics lies in the difficulty of properly accounting for the dynamical aspects of proteins, which are often critical to their functional mechanisms. Among the tools developed to deal with this issue, the Torsional Network Model (TNM) relies on internal degrees of freedom (torsion angles of the protein backbone), and can give a description of the thermal fluctuations of a protein structure, as well as generate structural ensembles. However, the TNM is a coarse-grained model that cannot ensure that the newly created conformations are exempt from any structural defects. Therefore, the main hypothesis of this project is that TNM assembly process can be improved. The ability to generate high-quality structural ensembles describing the dynamical properties of a protein would indeed be highly valuable in various applications. In this thesis, we create, evaluate and refine TNM ensembles from a set of reference protein structures defined experimentally (Levin et al., 2007). An approximation used in Bastolla and Dehouck, 2019, is developed: the evaluation is performed by Molprobity analysis, and the refinement is done by SIDEpro. Furthermore, a new approach is taken when refining the ensembles by Energy Minimization (EM). The results show a potential improvement of the TNM ensembles when adjusting the target RMSD to the protein studied; point to a enhancement when using side-chain reconstructions , and to its combination with Energy Minimization as a way to optimize the structure quality. On the other hand, the pros and cons of the followed methodology are discussed, because the use of the available static-protein oriented measures and methods makes specially important to beware of their limitations when applied to the protein-dynamic oriented TNM. Exploring further target RMSD values, adjusting them to specific protein dynamic simulations or replicating the same pipe-line in different data-sets are some of the proposals for future work. Furthermore, taking into account variables like the temperature, the flexibility of the protein, and the estimated optimal RMSD would be interesting for the next studies

    Verifying web applications : from business level specifications to automated model-based testing

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    One of reasons preventing a wider uptake of model-based testing in the industry is the difficulty which is encountered by developers when trying to think in terms of properties rather than linear specifications. A disparity has traditionally been perceived between the language spoken by customers who specify the system and the language required to construct models of that system. The dynamic nature of the specifications for commercial systems further aggravates this problem in that models would need to be rechecked after every specification change. In this paper, we propose an approach for converting specifications written in the commonly-used quasi-natural language Gherkin into models for use with a model-based testing tool. We have instantiated this approach using QuickCheck and demonstrate its applicability via a case study on the eHealth system, the national health portal for Maltese residents.peer-reviewe
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