Academic literature on the topic 'Evolutionary test'

As the complexity of modern day integrated circuits rises, many of the challenges associated with digital testing rise exponentially. VLSI technology continues to advance at a rapid pace, in accordance with Moore's Law, posing evermore complex, NP-complete problems for the test community. The testing of ICs currently accounts for approximately a third of the overall design costs and according to the Semiconductor Industry Association, the per-transistor test cost will soon exceed the per-transistor production cost. Given the need to test ICs of ever-increasing complexity and to contain the cost of test, the problems of test pattern generation, testability analysis and test set minimisation continue to provide formidable challenges for the research community. This thesis presents original work in these three areas. Firstly, a new method is presented for generating test patterns for multiple output combinational circuits based on the Boolean difference method and cubical calculus. The Boolean difference method has been largely overlooked in automatic test pattern generation algorithms due to its cumbersome, algebraic nature. It is shown that cubical calculus provides an elegant and economical technique for solving Boolean difference equations. Formal mathematical techniques are presented involving the Boolean difference and cubical calculus providing, a test pattern generation method that dispenses with the need for costly circuit simulations. The methods provide the basis for test generation algorithms which are suitable for computer implementation. Secondly, some of the core test pattern generation computations outlined above also provide the basis of a new method for computing testability measures such as controllability and observability. This method is effectively a very economical spin-off of the test pattern generation process using Boolean differences and cubical calculus. The third and largest part of this thesis introduces a new test set minimization algorithm, GA-MITS, based on an evolutionary optimization algorithm. This novel approach applies a genetic algorithm to find minimal or near minimal test sets while maintaining a given fault coverage. The algorithm is designed as a postprocessor to minimise test sets that have been previously generated by an ATPG system and is thus considered a static approach to the test set minimisation problem. It is shown empirically that GA-MITS is remarkably successful in minimizing test sets generated for the ISCAS-85 benchmark circuits and hence potentially capable of reducing the production costs of realistic digital circuits.

The purpose of this research was to determine the condition of evolution education in American parochial schools and the effect of evolution education on students' attitudes toward science. Data were gathered using Eraser's Test of Science Related Attitudes (TOSRA) and Bilica's Teaching Evolutionary Topics Survey (TETS). The research participants consisted of 60.3% of biology teachers currently teaching in Lutheran high schools in the United States, and 479 Lutheran high school biology students grades 9-12 in California, Nevada, and Arizona. In the first attitudinal study done specifically on parochial students, statistical analysis confirmed the reliability and validity of the TOSRA instrument for parochial school students. In a quasi-experimental design, analysis revealed that student science attitudes do change as a result of participating in a unit on evolution in the first year biology classes of secondary parochial schools. The emphasis placed by teachers on particular evolutionary topics was also analysed. It was found that all Lutheran high school biology teachers present evolution to some extent although not all topics are emphasized equally. The results also demonstrate that parochial school teachers have nearly the same emphasis placed on evolution as do public school teachers.

Made available in DSpace on 2019-03-30T00:01:20Z (GMT). No. of bitstreams: 0 Previous issue date: 2017-09-22<br>Some software systems must respond to thousands or millions of concurrent requests. These systems must be properly tested to ensure that they can function correctly under the expected load. Performance degradation and consequent system failures usually arise in stressed conditions. Stress testing subjects the program to heavy loads. Stress tests di¿er from other kinds of testing in that the system is executed on its breakpoints, forcing the application or the supporting infrastructure to fail. The search for the longest execution time is seen as a discontinuous, nonlinear, optimization problem, with the input domain of the system under test as a search space. In this context, search-based testing is viewed as a promising approach to verify timing constraints. Search-based software testing is the application of metaheuristic search techniques to generate software tests. The test adequacy criterion is transformed into a ¿tness function and a set of solutions in the search space is evaluated with respect to the ¿tness functionusingametaheuristic. Search-basedstresstestinginvolves¿ndingthebest-andworst-case executiontimestoascertainwhethertimingconstraintsareful¿lled. ServiceLevelAgreements (SLAs) are documents that specify realistic performance guarantees as well as penalties for non-compliance. SLAsaremadebetweenprovidersandcustomersthatincludeservicequality, resourcescapability,scalability,obligations,andconsequencesincaseofviolations. Satisfying SLAisofgreatimportanceandachallengingissue. Themainmotivationofthisthesisisto¿nd theadequateresponsetimeofSLAsusingStressTesting. Thisthesisaddressesthreeapproaches insearch-basedstresstests. First,HybridmetaheuristicusesTabuSearch,SimulatedAnnealing, andGeneticAlgorithmsinacollaborativemanner. Second,anapproachcalledHybridQusesa reinforcementlearningtechniquetooptimizethechoiceofneighboringsolutionstoexplore,reducingthetimeneededtoobtainthescenarioswiththelongestresponsetimeintheapplication. The best solutions found by HybridQ were on average 5.98% better that achieved by the Hybrid approach without Q-learning. Third, the thesis investigates the use of the multi-objective NSGA-II,SPEA2,PAESandMOEA/Dalgorithms. MOEA/Dmetaheuristicsobtainedthebest hypervolume value when compared with other approaches. The collaborative approach using MOEA/D and HybridQ improves the hypervolume values obtained and found more relevant workloadsthanthepreviousexperiments. AtoolnamedIAdapter,aJMeterpluginforperformingsearch-basedstresstests,wasdevelopedandusedtoconductalltheexperiments. Keywords: Search-based Testing, Stress Testing, Multi-objective metaheuristics, Hybrid metaheuristics,ReinforcementLearning.<br>Alguns sistemas de software devem responder a milhares ou milhões de requisições simultâneos. Tais sistemas devem ser devidamente testados para garantir que eles possam funcionar corretamente sob uma carga esperada. Normalmente, a degradação do desempenho e consequentes falhas do sistema geralmente ocorrem em condições de estresse. No teste de estresse o sistema é submetido a cargas de trabalho acima dos resquistos não funcionais estabelecidos. Os testes de estresse diferem de outros tipos de testes em que o sistema é executado em seus pontos de interrupção, forçando o aplicativo ou a infra-estrutura de suporte a falhar. Testes de estresse podem ser vistos como um problema de otimização descontínuo, não-linear, comodomínio de entrada do sistema em test ecomo espaço de busca. Neste contexto,ostestes baseados em busca (search-based tests) são vistos como uma abordagem promissora para veri¿car as restrições de tempo. O teste de software baseado em busca é a aplicação de técnicas de pesquisa metaheurística para gerar testes de software. O critério de adequação do teste é transformado em uma função objetivo e um conjunto de soluções no espaço de busca é avaliado em relação à função objetivo usando uma metaheurística. Otestedeestressebaseadoembusca envolve encontrar os tempos de execução melhores e piores para veri¿car se as restrições de tempo são cumpridas. Os acordos de nível de serviço (SLA) são documentos que especi¿cam garantias de desempenho realistas, bem como penalidades por incumprimento. Os SLAs são feitos entre provedores e clientes que incluem qualidade do serviço, capacidade de recursos, escalabilidade, obrigações e consequencias em caso de violação. Satisfazer o SLA é de grande importância e um problema desa¿ador. A principal motivação desta tese é encontrar o tempo de resposta adequado dos SLAs usando teste de estresse. Esta tese apresenta três abordagens em testes de estresse baseados em busca. Primeiro, a metaheurística híbrida usa Tabu Search, Simulated Annealing e Algoritmos Genéticos de forma colaborativa. Em segundo lugar, uma abordagem chamada HybridQ usa uma técnica de aprendizado de reforço para otimizar a escolha de soluções vizinhas para explorar, reduzindo o tempo necessário para obter os cenários com o tempo de resposta mais longo na aplicação. As melhores soluções encontradas pelo HybridQ foram em média 5,98 % melhores que alcançadas pela abordagem híbrida sem Qlearning. Em terceiro lugar, a tese investiga o uso dos algoritmos multi-objetivos NSGA-II, SPEA2, PAES e MOEA/D. A metaheurística MOEA/D obteve o melhor valor de hipervolume quando comparada com outras abordagens. A abordagem colaborativa usand oMOEA/DeHybridQ melhora os valores de hipervolume obtidos e encontrou workloads mais relevantes do que as experiências anteriores. Uma ferramenta chamada IAdapter, um plugin JMeter para realizar testes de esforço baseados em busca, foi desenvolvida e usada para realizar todas as experiências. Palavras-chave: Search-basedTesting,StressTesting,Multi-objective metaheuristics,Hybridmetaheuristics,ReinforcementLearning

Many-objective optimisation poses great challenges to evolutionary algorithms. To start with, the ineffectiveness of the Pareto dominance relation, which is the most important criterion in multi-objective optimisation, results in the underperformance of traditional Pareto-based algorithms. Also, the aggravation of the conflict between proximity and diversity, along with increasing time or space requirement as well as parameter sensitivity, has become key barriers to the design of effective many-objective optimisation algorithms. Furthermore, the infeasibility of solutions' direct observation can lead to serious difficulties in algorithms' performance investigation and comparison. In this thesis, we address these challenges, aiming to make evolutionary algorithms as effective in many-objective optimisation as in two- or three-objective optimisation. First, we significantly enhance Pareto-based algorithms to make them suitable for many-objective optimisation by placing individuals with poor proximity into crowded regions so that these individuals can have a better chance to be eliminated. Second, we propose a grid-based evolutionary algorithm which explores the potential of the grid to deal with many-objective optimisation problems. Third, we present a bi-goal evolution framework that converts many objectives of a given problem into two objectives regarding proximity and diversity, thus creating an optimisation problem in which the objectives are the goals of the search process itself. Fourth, we propose a comprehensive performance indicator to compare evolutionary algorithms in optimisation problems with various Pareto front shapes and any objective dimensionality. Finally, we construct a test problem to aid the visual investigation of evolutionary search, with its Pareto optimal solutions in a two-dimensional decision space having similar distribution to their images in a higher-dimensional objective space. The work reported in this thesis is the outcome of innovative attempts at addressing some of the most challenging problems in evolutionary many-objective optimisation. This research has not only made some of the existing approaches, such as Pareto-based or grid-based algorithms that were traditionally regarded as unsuitable, now effective for many-objective optimisation, but also pushed other important boundaries with novel ideas including bi-goal evolution, a comprehensive performance indicator and a test problem for visual investigation. All the proposed algorithms have been systematically evaluated against existing state of the arts, and some of these algorithms have already been taken up by researchers and practitioners in the field.