Genetic algorithm with a Bayesian approach for the detection of multiple points of change of time series of counting exceedances of specific thresholds

Although the applications of Non-Homogeneous Poisson Processes to model and study the threshold overshoots of interest in different time series of measurements have proven to provide good results, they needed to be complemented with an efficient and automatic diagnostic technique to establish the location of the change-points, which, when taken into account, make the estimated model fit poorly in regards of the information contained in the real model. For this reason, we propose a new method to solve the segmentation uncertainty of the time series of measurements, where the emission distribution of exceedances of a specific threshold is the focus of investigation. One of the great contributions of the present algorithm is that all the days that overflowed are candidates to be a change-point, so all the possible configurations of overflow days are the possible chromosomes, which will unite to have offspring. Under the heuristics of a genetic algorithm, the solution to the problem of finding such change points will be guaranteed to be non-local and the best possible one, reducing wasted machine time evaluating the least likely chromosomes to be a solution to the problem. The analytical evaluation technique will be by means of the Minimum Description Length (\textit{MDL}) as the objective function, which is the joint posterior distribution function of the parameters of each regime and the change points that determines them and which account as well for the influence of the presence of said times

Integration of software reliability into systems reliability optimization

Reliability optimization originally developed for hardware systems is extended to incorporate software into an integrated system reliability optimization. This hardware-software reliability optimization problem is formulated into a mixed-integer programming problem. The integer variables are the number of redundancies, while the real variables are the components reliabilities;To search a common framework under which hardware systems and software systems can be combined, a review and classification of existing software reliability models is conducted. A software redundancy model with common-cause failure is developed to represent the objective function. This model includes hardware redundancy with independent failure as a special case. A software reliability-cost function is then derived based on a binomial-type software reliability model to represent the constraint function;Two techniques, the combination of heuristic redundancy method with sequential search method, and the Lagrange multiplier method with the branch-and-bound method, are proposed to solve this mixed-integer reliability optimization problem. The relative merits of four major heuristic redundancy methods and two sequential search methods are investigated through a simulation study. The results indicate that the sequential search method is a dominating factor of the combination method. Comparison of the two proposed mixed-integer programming techniques is also studied by solving two numerical problems, a series system with linear constraints and a bridge system with nonlinear constraints. The Lagrange multiplier method with the branch-and-bound method has been shown to be superior to all other existing methods in obtaining the optimal solution;Finally an illustration is performed for integrating software reliability model into systems reliability optimization

Weighted Statistical Testing based on Active Learning and Formal Verification Techniques for Software Reliability Assessment

This work developed an automatic approach for the assessment of software reliability which is both theoretical sound and practical. The developed approach extends and combines theoretical sound approaches in a novel manner to systematically reduce the overhead of reliability assessment

Mecanismos de previsão de perda de deadline para tratadores de eventos RTSJ

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Ciência da Computação, Florianópolis, 2014Estrategias para estimar a probabilidade de deadlines firmes serem alcançados são fundamentais porque permitem a realização de ações corretivas para a melhoria do desempenho do sistema. Este tipo de estrategia permite a avaliação de sistemas de tempo real que estão em funcionamento, principalmente quando ha alguma mudança quanto ao projeto inicial, ou mesmo avaliar novos projetos para analisar se as restrições temporais foram definidas adequadamente. Nesta dissertação são apresentados dois mecanismos de previsão de perda de deadline em sistemas monoprocessados e multiprocessados de tempo real firme. O Mecanismo de Previsão de Perda de Deadline Baseado na Folga (MBF) utiliza dados sobre o comportamento das tarefas (deadline, tempo de computação e o tempo de espera na fila de prontos do processador) para calcular a folga e determinar se o deadline pode ser cumprido. O Mecanismo de Previsão de Perda de Deadline Baseado no Histórico (MBH) utiliza regressão linear e relaciona dados de um histórico de execuções passadas, que possui o tamanho da fila de prontos do processador e seu respectivo tempo de resposta, com o tamanho atual da fila de prontos do processador para calcular o tempo de resposta previsto da thread e depois definir a probabilidade dela cumprir seu deadline. Sera apresentado um modelo de tarefas para aplicações não críticas em um sistema de tempo real firme que caracteriza uma aplicação real utilizada nos testes, o cruise control. Estes testes foram feitos utilizando uma implementação em Java RTS desta aplicação em um ambiente não especialista, próximo de um ambiente de tempo real comum, com varias configurações, buscando abranger uma grande gama de cenários. Após os testes, a qualidade das previsões é avaliada utilizando as Métricas Taxa Relativa de Erro e Taxa de Previsões Corretas. Os resultados demonstram que ambos os mecanismos trazem bons resultados em ambientes com cargas baixas, medias e altas, sendo o MBF um excelente previsor para sistemas monoprocessados e o MBH mais adequado aos sistemas multiprocessados.Abstract: Strategies to estimate the probability of rm deadlines be achieved areessential because they allow the use of corrective actions to improvesystem performance. This type of strategy allows the evaluation of realtimesystems that are in operation, especially when there is any changeon the initial design, or evaluate new projects to analyze whether thetemporal constraints were appropriately settled. In this dissertation,two deadline missing prediction mechanisms for rm real-time uniprocessorand multiprocessor systems are presented. The Deadline MissingPrediction Mechanism Based on Slack (MBF) uses data of tasks's behavior(deadline, computation time and the waiting time in the processorready queue) to calculate the slack and determine whether the deadlinecan be met. The Deadline Missing Prediction Mechanism Based onHistorical Data (MBH) uses linear regression and associates data froma past execution's historical, which is the size of the processor readyqueue and its associated response time, with the current size of processorready queue to calculate the predicted response time of the threadand then dene the probability of meeting its deadline. A model of tasksfor non-critical applications in a rm real-time system which characterizesa real application, similar to the cruise control, will be usedin the tests. These tests were done using an implementation in JavaRTS applied to a non-specialist environment as a common real-timeenvironment with various congurations scenarios. The quality of theforecasts is evaluated using the metrics Relative Error Rate and CorrectPrediction Rate. The results indicate that both mechanisms improve theperformance in environments with high, medium and low system loadwhereas the MBF being an adequate predictor for uniprocessor systemsand the MBH best suited to multiprocessor systems

Weighted Statistical Testing based on Active Learning and Formal Verification Techniques for Software Reliability Assessment

This thesis developed an automatic approach for the assessment of software reliability which is both theoretical sound and practical. The developed approach extends and combines theoretical sound approaches in a novel manner to systematically reduce the overhead of reliability assessment