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  • Ítem
    Incorporating coverage criteria in bounded exhaustive black box test generation of structural inputs
    (2011) Aguirre, Nazareno; Bengolea, Valeria; Galeotti, Juan Pablo; Frías, Marcelo
    "The automated generation of test cases for heap allocated, complex, structures is particularly difficult. Various state of the art tools tackle this problem by bounded exhaustive exploration of potential test cases, using constraint solving mechanisms based on techniques such as search, model checking, symbolic execution and combinations of these. In this article we present a technique for improving the bounded ex haustive constraint based test case generation of structurally complex inputs, for “filtering” approaches. The technique works by guiding the search considering a given black box test criterion. Such a test criterion is incorporated in the constraint based mechanism so that the exploration of potential test cases can be pruned without missing coverable classes of inputs, corresponding to the test criterion. We present the technique, together with some case studies illustrating its performance for some black box testing criteria. The experimental results associated with these case studies are shown in the context of Korat, a state of the art tool for constraint based test case generation, but the approach is applicable in other contexts using a filtering approach to test generation."
  • Ítem
    Clasificación de Imágenes SAR utilizando descriptores de textura
    (2021-10) Gambini, Juliana; Rey, Andrea; Delrieux, Claudio
    "Las imágenes SAR (Sythetic Aperture Radar) y PolSAR (Polarimetric Synthetic Aperture Radar) cumplen un rol fundamental en el monitoreo ambiental y observación terrestre debido a que proveen información que las imágenes ópticas no proporcionan. Sin embargo, estas imágenes están contaminadas con un ruido inherente al méetodo de captura llamado ruido speckle que dificulta su análisis e interpretación automática. Los modelos avanzados de segmentación de imágenes SAR están dedicados a resolver las dificultades que este ruido provoca. En este sentido, resulta de suma importancia el estudio de parámetros que describan las características estructurales de textura de imagen en presencia de ruido speckle y permitan su interpretación automática. En este trabajo, se propone un nuevo modelo de clasificación de imágenes SAR basado en el cálculo de descriptores de textura locales, formando un vector característico, el cual involucra estimaciones de parámetros de una distribución de probabilidad, estimaciones de la dimensión fractal y entropía de Tsallis. Luego, el etiquetado de cada pixel se realiza utilizando el método de clasificación supervisada SVM (Support Vector Machine). Se analizan los resultados de aplicar el algoritmo propuesto en imágenes SAR sintéticas, simples y con valores extremos agregados, los cuales resultan altamente prometedores para aplicarse en imágenes reales."
  • Ítem
    FLACK: Counterexample-guided fault localization for alloy models
    (2021) Zheng, Guolong; Nguyen, Thanh Vu; Gutiérrez Brida, Simón; Regis, Germán; Frías, Marcelo; Aguirre, Nazareno; Bagher, Hamid
    "Fault localization is a practical research topic that helps developers identify code locations that might cause bugs in a program. Most existing fault localization techniques are designed for imperative programs (e.g., C and Java) and rely on analyzing correct and incorrect executions of the program to identify suspicious statements. In this work, we introduce a fault localization approach for models written in a declarative language, where the models are not “executed,” but rather converted into a logical formula and solved using backend constraint solvers. We present FLACK, a tool that takes as input an Alloy model consisting of some violated assertion and returns a ranked list of suspicious expressions contributing to the assertion violation. The key idea is to analyze the differences between counterexamples, i.e., instances of the model that do not satisfy the assertion, and instances that do satisfy the assertion to find suspicious expressions in the input model. The experimental results show that FLACK is efficient (can handle complex, real world Alloy models with thousand lines of code within 5 seconds), accurate (can consistently rank buggy expressions in the top 1.9% of the suspicious list), and useful (can often narrow down the error to the exact location within the suspicious expressions)."
  • Ítem
    Discovering sensing capability in multi-agent systems
    (2010) Parpaglione, María Cristina; Santos, Juan Miguel
    "What should be the sensing capabilities of agents in a Multi-Agent System be to solve a problem efficiently, quickly and economicly? This question often appears when trying to solve a problem using Multi-Agent Systems. This paper introduces a method to find these sensing capabilities in order to solve a given problem. To achieve this, the sensing capability of an agent is modeled by a parametrized function and then Genetic Algorithms are used to find the parameters’ values. The individual behavior of the agents are found with Reinforcement Learning."
  • Ítem
    EvoSpex: An evolutionary algorithm for learning postconditions (artifact)
    (2021) Molina, Facundo; Ponzio, Pablo; Aguirre, Nazareno; Frías, Marcelo
    "Having the expected behavior of software specified in a formal language can greatly improve the automation of software verification activities, since these need to contrast the intended behavior with the actual software implementation. Unfortunately, software many times lacks such specifications, and thus providing tools and techniques that can assist developers in the construction of software specifications are relevant in software engineering. As an aid in this context, we present EvoSpex, a tool that given a Java method, automatically produces a specification of the method’s current behavior, in the form of postcondition assertions. EvoSpex is based on generating software runs from the implementation (valid runs), making modifications to the runs to build divergent behaviors (invalid runs), and executing a genetic algorithm that tries to evolve a specification to satisfy the valid runs, and leave out the invalid ones. Our tool supports a rich JML-like assertion language, that can capture complex specifications, including sophisticated object structural properties."