Performance requirements verification during software systems development

Requirements verification refers to the assurance that the implemented system reflects the specified requirements. Requirement verification is a process that continues through the life cycle of the software system. When the software crisis hit in 1960, a great deal of attention was placed on the verification of functional requirements, which were considered to be of crucial importance. Over the last decade, researchers have addressed the importance of integrating non-functional requirement in the verification process. An important non-functional requirement for software is performance. Performance requirement verification is known as Software Performance Evaluation. This thesis will look at performance evaluation of software systems. The performance evaluation of software systems is a hugely valuable task, especially in the early stages of a software project development. Many methods for integrating performance analysis into the software development process have been proposed. These methodologies work by utilising the software architectural models known in the software engineering field by transforming these into performance models, which can be analysed to gain the expected performance characteristics of the projected system. This thesis aims to bridge the knowledge gap between performance and software engineering domains by introducing semi-automated transformation methodologies. These are designed to be generic in order for them to be integrated into any software engineering development process. The goal of these methodologies is to provide performance related design guidance during the system development. This thesis introduces two model transformation methodologies. These are the improved state marking methodology and the UML-EQN methodology. It will also introduce the UML-JMT tool which was built to realise the UML-EQN methodology. With the help of automatic design models to performance model algorithms introduced in the UML-EQN methodology, a software engineer with basic knowledge of performance modelling paradigm can conduct a performance study on a software system design. This was proved in a qualitative study where the methodology and the tool deploying this methodology were tested by software engineers with varying levels of background, experience and from different sectors of the software development industry. The study results showed an acceptance for this methodology and the UML-JMT tool. As performance verification is a part of any software engineering methodology, we have to define frame works that would deploy performance requirements validation in the context of software engineering. Agile development paradigm was the result of changes in the overall environment of the IT and business worlds. These techniques are based on iterative development, where requirements, designs and developed programmes evolve continually. At present, the majority of literature discussing the role of requirements engineering in agile development processes seems to indicate that non-functional requirements verification is an unchartered territory. CPASA (Continuous Performance Assessment of Software Architecture) was designed to work in software projects where the performance can be affected by changes in the requirements and matches the main practices of agile modelling and development. The UML-JMT tool was designed to deploy the CPASA Performance evaluation tests

Software Perfomance Assessment at Architectural Level: A Methodology and its Application

Las arquitecturas software son una valiosa herramienta para la evaluación de las propiedades cualitativas y cuantitativas de los sistemas en sus primeras fases de desarrollo. Conseguir el diseño adecuado es crítico para asegurar la bondad de dichas propiedades. Tomar decisiones tempranas equivocadas puede implicar considerables y costosos cambios en un futuro. Dichas decisiones afectarían a muchas propiedades del sistema, tales como su rendimiento, seguridad, fiabilidad o facilidad de mantenimiento. Desde el punto de vista del rendimiento software, la ingeniería del rendimiento del software (SPE) es una disciplina de investigación madura y comúnmente aceptada que propone una evaluación basada en modelos en las primeras fases del ciclo de vida de desarrollo software. Un problema en este campo de investigación es que las metodologías hasta ahora propuestas no ofrecen una interpretación de los resultados obtenidos durante el análisis del rendimiento, ni utilizan dichos resultados para proponer alternativas para la mejora de la propia arquitectura software. Hasta la fecha, esta interpretación y mejora requiere de la experiencia y pericia de los ingenieros software, en especial de expertos en ingeniería de prestaciones. Además, a pesar del gran número de propuestas para evaluar el rendimiento de sistemas software, muy pocos de estos estudios teóricos son posteriormente aplicados a sistemas software reales. El objetivo de esta tesis es presentar una metodología para el asesoramiento de decisiones arquitecturales para la mejora, desde el punto de vista de las prestaciones, de las sistemas software. La metodología hace uso del Lenguaje Unificado de Modelado (UML) para representar las arquitecturas software y de métodos formales, concretamente redes de Petri, como modelo de prestaciones. El asesoramiento, basado en patrones y antipatrones, intenta detectar los principales problemas que afectan a las prestaciones del sistema y propone posibles mejoras para mejoras dichas prestaciones. Como primer paso, estudiamos y analizamos los resultados del rendimiento de diferentes estilos arquitectónicos. A continuación, sistematizamos los conocimientos previamente obtenidos para proponer una metodología y comprobamos su aplicabilidad asesorando un caso de estudio real, una arquitectura de interoperabilidad para adaptar interfaces a personas con discapacidad conforme a sus capacidades y preferencias. Finalmente, se presenta una herramienta para la evaluación del rendimiento como un producto derivado del propio ciclo de vida software

Spatial quantification and mathematical modelling of tissue development

In this thesis, we study biological tissue development, during which cells organise themselves into structures which perform a specific function. Understanding how particular types of mechanisms lead to the emergence of various cell patterns in tissues is the main motivation of this research. Quantifying the tissue patterns is a first step towards understanding which mechanisms are at work in particular experiments. For this purpose, we develop pair-correlation functions (PCFs) which quantify how a spatial distribution of cells deviates from complete spatial randomness over specified directions. We evaluate the usefulness of PCFs for studying the three-dimensional organisation of cells in tumour spheroids and show that the PCFs robustly reveal information about their spatial structure. In particular, we demonstrate that the boundary that separates the necrotic and viable zones in the tumour spheroids can be detected using the PCF with a high degree of accuracy. We then turn to development of mathematical models to investigate the types of patterns that can arise from simple hypothesised interactions between cells. We begin in Chapter 3 by developing an on-lattice agent-based model (ABM) to investigate tumour spheroid growth using two different culture methods: suspension culture, and culture within a microgel. Our results suggest that stratifying the seeded cells into multiple layers and also reducing cell death are the key effects of the microgel that enable it to produce more uniformly-sized spheroids. In Chapter 4, we extend the ABM to study systems with two interacting species. A huge variety of aggregation patterns can arise in these systems, depending upon the underlying attractive-repulsive mechanisms. More specifically, we show that the run-and chase mechanism can produce a striped pattern, similar to that observed on the skin of zebrafish. Finally, we develop a non-local continuous model, approximating the mean behaviour of the ABM. This provides a connection between the cell-level and population-level models of tissue development. A linear stability analysis of the continuous model allows us to investigate parameter regimes that produce striped patterns. Importantly, we also point out the disparities that may arise between the behaviours of the continuous and discrete models, which highlights the importance of considering the underlying biological constraints in using the continuous approximated models. In particular, we show that the derivation of the approximate continuum model from the ABM introduces terms representing cell-size effects. These terms can lead to the emergence of stripes in cases where they would not be predicted in the similar continuum model of Painter et al. (2015), which does not include these terms. The combination of spatial quantification and mathematical modelling (using both continuous and discrete methods) developed in this work helps us to gain a better understanding of tissue development. Our approach provides a novel means to investigate the underpinning mechanisms of tissue development by combining model simulations with analysis of biological and synthetic data using the pair-correlation functions.Thesis (Ph.D.) -- University of Adelaide, School of Mathematical Sciences, 201

Enabling Automated, Reliable and Efficient Aerodynamic Shape Optimization With Output-Based Adapted Meshes

Simulation-based aerodynamic shape optimization has been greatly pushed forward during the past several decades, largely due to the developments of computational fluid dynamics (CFD), geometry parameterization methods, mesh deformation techniques, sensitivity computation, and numerical optimization algorithms. Effective integration of these components has made aerodynamic shape optimization a highly automated process, requiring less and less human interference. Mesh generation, on the other hand, has become the main overhead of setting up the optimization problem. Obtaining a good computational mesh is essential in CFD simulations for accurate output predictions, which as a result significantly affects the reliability of optimization results. However, this is in general a nontrivial task, heavily relying on the user’s experience, and it can be worse with the emerging high-fidelity requirements or in the design of novel configurations. On the other hand, mesh quality and the associated numerical errors are typically only studied before and after the optimization, leaving the design search path unveiled to numerical errors. This work tackles these issues by integrating an additional component, output-based mesh adaptation, within traditional aerodynamic shape optimizations. First, we develop a more suitable error estimator for optimization problems by taking into account errors in both the objective and constraint outputs. The localized output errors are then used to drive mesh adaptation to achieve the desired accuracy on both the objective and constraint outputs. With the variable fidelity offered by the adaptive meshes, multi-fidelity optimization frameworks are developed to tightly couple mesh adaptation and shape optimization. The objective functional and its sensitivity are first evaluated on an initial coarse mesh, which is then subsequently adapted as the shape optimization proceeds. The effort to set up the optimization is minimal since the initial mesh can be fairly coarse and easy to generate. Meanwhile, the proposed framework saves computational costs by reducing the mesh size at the early stages of the optimization, when the design is far from optimal, and avoiding exhaustive search on low-fidelity meshes when the outputs are inaccurate. To further improve the computational efficiency, we also introduce new methods to accelerate the error estimation and mesh adaptation using machine learning techniques. Surrogate models are developed to predict the localized output error and optimal mesh anisotropy to guide the adaptation. The proposed machine learning approaches demonstrate good performance in two-dimensional test problems, encouraging more study and developments to incorporate them within aerodynamic optimization techniques. Although CFD has been extensively used in aircraft design and optimization, the design automation, reliability, and efficiency are largely limited by the mesh generation process and the fixed-mesh optimization paradigm. With the emerging high-fidelity requirements and the further developments of unconventional configurations, CFD-based optimization has to be made more accurate and more efficient to achieve higher design reliability and lower computational cost. Furthermore, future aerodynamic optimization needs to avoid unnecessary overhead in mesh generation and optimization setup to further automate the design process. The author expects the methods developed in this work to be the keys to enable more automated, reliable, and efficient aerodynamic shape optimization, making CFD-based optimization a more powerful tool in aircraft design.PHDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163034/1/cgderic_1.pd

Analogue filter networks: developments in theory, design and analyses

Not availabl

The application of artificial neural networks to interpret acoustic emissions from submerged arc welding

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Automated fusion welding processes play a fundamental role in modern manufacturing industries. The proliferation of joint geometries together with the large permutation of associated process variable configurations has given rise to research into complex system modelling and control strategies. Many of these techniques have involved monitoring of not only the electrical characteristics of the process but visual and acoustic information. Acoustic information derived from certain welding processes is well documented as it is an established fact that skilled manual welders utilise such information as an aid to creating an optimum weld. The experimental investigation presented in this thesis is dedicated to the feasibility of monitoring airborne acoustic emissions of Submerged Arc Welding (SAW) for diagnostic and real time control purposes. The experimental method adopted for this research takes a cybernetic approach to data processing and interpretation in an attempt to replicate the robustness of human biological functions. A custom designed audio hardware system was used to analyse signals obtained from bead on mild steel plate fusion welds. Time and frequency domains were used in an attempt to establish salient characteristics or identify the signatures associated with changes of the process variables. The featured parameters were voltage / current and weld travel speed, due to their ease of validation. However, consideration has also been given to weld defect prediction due to process instabilities. As the data proved to be highly correlated and erratic when subjected to off line statistical analysis, extensive investigation was given to the application of artificial neural networks to signal processing and real time control scenarios. As a consequence, a dedicated neural based software system was developed, utilising supervised and unsupervised neural techniques to monitor the process. The research was aimed at proving the feasibility of monitoring the electrical process parameters and stability of the welding process in real time. It was shown to be possible, by the exploitation of artificial neural networks, to generate a number of monitoring parameters indicative of the welding process state. The limitations of the present neural method and proposed developments are discussed, together with an overview of applied neural network technology and its impact on artificial intelligence and robotic control. Further developments are considered together with recommendations for future areas of research

Bringing Model Checking Closer To Practical Software Engineering

Bal, H.E. [Promotor]Templon, J.A. [Copromotor]Willemse, T.A.C. [Copromotor

Multi-objective optimization of Tension Leg Platform using evolutionary algorithm based on surrogate model

An Innovative Tension Leg Platform (TLP) Optimization Program, called ITOP, has been developed to solve the multi-objective optimization problem for TLP. We first examine the hydrodynamic behavior of a base TLP for wave headings between 0∘ and 45∘. The numerical results show that the maximum heave and surge motion responses occur in 0∘ wave heading in long-crest waves. It is found that the dynamic tension of No. 8 tendon is larger than the other tendons and reaches its maximum in 45∘ wave heading. It can be attributed to the fact that heave and pitch motions are almost out of phase for wave periods between 10 and 15 s. Because the maximum wave elevation occurs near the northeast column and the vertical motion is very small, the minimum airgap occurs there. Moreover, a surrogate model based on radial basis function (RBF) has been built and adopted to estimate the hydrodynamic performance of TLP. A multi-objective evolutionary algorithm, Non-dominated Sorting Genetic Algorithm II (NSGAII), is employed to find the Pareto-optimal solutions. By comprehensive and systematic computations and analyses, it is revealed that the maximum dynamic tension shows positive correlation with pontoon height and width, but negative correlation with hull draft, column spacing, and column diameter. The most efficient modification strategy for design is proposed to reduce the maximum dynamic tendon tension. According to the strategy, the column spacing, draft, and column diameter should be increased in sequence. By applying this strategy, the maximum dynamic tendon tensions can be reduced while the total weight of the platform is minimized as much as possible