18 research outputs found

    A Requirements-Based Partition Testing Framework Using Particle Swarm Optimization Technique

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    Modern society is increasingly dependent on the quality of software systems. Software failure can cause severe consequences, including loss of human life. There are various ways of fault prevention and detection that can be deployed in different stages of software development. Testing is the most widely used approach for ensuring software quality. Requirements-Based Testing and Partition Testing are two of the widely used approaches for testing software systems. Although both of these techniques are mature and are addressed widely in the literature and despite the general agreement on both of these key techniques of functional testing, a combination of them lacks a systematic approach. In this thesis, we propose a framework along with a procedural process for testing a system using Requirements-Based Partition Testing (RBPT). This framework helps testers to start from the requirements documents and follow a straightforward step by step process to generate the required test cases without loosing any required data. Although many steps of the process are manual, the framework can be used as a foundation for automating the whole test case generation process. Another issue in testing a software product is the test case selection problem. Choosing appropriate test cases is an essential part of software testing that can lead to significant improvements in efficiency, as well as reduced costs of combinatorial testing. Unfortunately, the problem of finding minimum size test sets is NP-complete in general. Therefore, artificial intelligence-based search algorithms have been widely used for generating near-optimal solutions. In this thesis, we also propose a novel technique for test case generation using Particle Swarm Optimization (PSO), an effective optimization tool which has emerged in the last decade. Empirical studies show that in some domains particle swarm optimization is equally well-suited or even better than some other techniques. At the same time, a particle swarm algorithm is much simpler, easier to implement, and has just a few parameters that the user needs to adjust. These properties make PSO an ideal technique for test case generation. In order to have a fair comparison of our newly proposed algorithm against existing techniques, we have designed and implemented a framework for automatic evaluation of these methods. Through experiments using our evaluation framework, we illustrate how this new test case generation technique can outperform other existing methodologies

    Information Sharing Impact of Stochastic Diffusion Search on Population-Based Algorithms

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    This work introduces a generalised hybridisation strategy which utilises the information sharing mechanism deployed in Stochastic Diffusion Search when applied to a number of population-based algorithms, effectively merging this nature-inspired algorithm with some population-based algorithms. The results reported herein demonstrate that the hybrid algorithm, exploiting information-sharing within the population, improves the optimisation capability of some well-known optimising algorithms, including Particle Swarm Optimisation, Differential Evolution algorithm and Genetic Algorithm. This hybridisation strategy adds the information exchange mechanism of Stochastic Diffusion Search to any population-based algorithm without having to change the implementation of the algorithm used, making the integration process easy to adopt and evaluate. Additionally, in this work, Stochastic Diffusion Search has also been deployed as a global optimisation algorithm, and the optimisation capability of two newly introduced minimised variants of Particle Swarm algorithms is investigated

    CBM Progress Report 2009

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    Gaseous Radiation Detectors

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    Widely used in high-energy and particle physics, gaseous radiation detectors are undergoing continuous development. The first part of this book provides a solid background for understanding the basic processes leading to the detection and tracking of charged particles, photons, and neutrons. Continuing then with the development of the multi-wire proportional chamber, the book describes the design and operation of successive generations of gas-based radiation detectors, as well as their use in experimental physics and other fields. Examples are provided of applications for complex events tracking, particle identification, and neutral radiation imaging. Limitations of the devices are discussed in detail. Including an extensive collection of data and references, this book is ideal for researchers and experimentalists in nuclear and particle physics. It has been reissued as an Open Access publication

    Gaseous Radiation Detectors

    Get PDF
    Widely used in high-energy and particle physics, gaseous radiation detectors are undergoing continuous development. The first part of this book provides a solid background for understanding the basic processes leading to the detection and tracking of charged particles, photons, and neutrons. Continuing then with the development of the multi-wire proportional chamber, the book describes the design and operation of successive generations of gas-based radiation detectors, as well as their use in experimental physics and other fields. Examples are provided of applications for complex events tracking, particle identification, and neutral radiation imaging. Limitations of the devices are discussed in detail. Including an extensive collection of data and references, this book is ideal for researchers and experimentalists in nuclear and particle physics. It has been reissued as an Open Access publication

    Modeling and Simulation of Metallurgical Processes in Ironmaking and Steelmaking

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    In recent years, improving the sustainability of the steel industry and reducing its CO2 emissions has become a global focus. To achieve this goal, further process optimization in terms of energy and resource efficiency and the development of new processes and process routes are necessary. Modeling and simulation have established themselves as invaluable sources of information for otherwise unknown process parameters and as an alternative to plant trials that involves lower costs, risks, and time. Models also open up new possibilities for model-based control of metallurgical processes. This Special Issue focuses on recent advances in the modeling and simulation of unit processes in iron and steelmaking. It includes reviews on the fundamentals of modeling and simulation of metallurgical processes, as well as contributions from the areas of iron reduction/ironmaking, steelmaking via the primary and secondary route, and continuous casting

    Generalized averaged Gaussian quadrature and applications

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    A simple numerical method for constructing the optimal generalized averaged Gaussian quadrature formulas will be presented. These formulas exist in many cases in which real positive GaussKronrod formulas do not exist, and can be used as an adequate alternative in order to estimate the error of a Gaussian rule. We also investigate the conditions under which the optimal averaged Gaussian quadrature formulas and their truncated variants are internal

    MS FT-2-2 7 Orthogonal polynomials and quadrature: Theory, computation, and applications

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    Quadrature rules find many applications in science and engineering. Their analysis is a classical area of applied mathematics and continues to attract considerable attention. This seminar brings together speakers with expertise in a large variety of quadrature rules. It is the aim of the seminar to provide an overview of recent developments in the analysis of quadrature rules. The computation of error estimates and novel applications also are described
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