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@MastersThesis{Balera:2017:AlGeCa,
               author = "Balera, Juliana Marino",
                title = "Um algoritmo para gera{\c{c}}{\~a}o de casos de teste 
                         combinatorial via matriz de cobertura com n{\'{\i}}veis 
                         variados",
               school = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                 year = "2017",
              address = "S{\~a}o Jos{\'e} dos Campos",
                month = "2017-02-14",
             keywords = "teste de software, T-Tuple Reallocation, designs combinatoriais, 
                         matriz de cobertura com n{\'{\i}}veis variados, experimento 
                         controlado, software testing, T-Tuple Reallocation, combinatorial 
                         designs, mixed-level covering arrays, controlled experiment.",
             abstract = "Na perspectiva de sistemas complexos, como softwares desenvolvidos 
                         para aplica{\c{c}}{\~o}es espaciais tais como sat{\'e}lites, 
                         bal{\~o}es estratosf{\'e}ricos e foguetes, existem sempre riscos 
                         relacionados ao mau funcionamento do produto que podem causar 
                         danos ao meio ambiente, grandes perdas financeiras, ou o pior, 
                         perda de vidas. Para minimizar ao m{\'a}ximo esses riscos, {\'e} 
                         necess{\'a}rio que o processo de teste desses sistemas ocorra de 
                         forma rigorosa e eficiente. Como n{\~a}o {\'e} poss{\'{\i}}vel 
                         testar tais produtos exaustivamente, dada a larga gama de casos de 
                         teste poss{\'{\i}}veis, {\'e} fundamental, portanto, que se 
                         tenham dispon{\'{\i}}veis m{\'e}todos para a 
                         gera{\c{c}}{\~a}o/sele{\c{c}}{\~a}o de casos de teste que 
                         possuem grande potencial de revela{\c{c}}{\~a}o de defeitos, e 
                         que possuam custo reduzido. Nessa dire{\c{c}}{\~a}o, designs 
                         combinatoriais v{\^e}m chamando aten{\c{c}}{\~a}o da comunidade 
                         de teste de software para gerar conjuntos de casos de testes 
                         menores (menor custo para executar) e eficientes (capacidade de 
                         encontrar defeitos no software). Diante disso, essa 
                         disserta{\c{c}}{\~a}o de mestrado tem como objetivo apresentar 
                         uma nova forma de gerar conjuntos de casos de teste via designs 
                         combinatoriais, sendo que tais casos de teste tenham custo menor e 
                         efici{\^e}ncia compar{\'a}vel {\`a} solu{\c{c}}{\~o}es 
                         j{\'a} existentes na literatura. Ent{\~a}o, um algoritmo, 
                         denominado T-Tuple Reallocation (TTR; Realoca{\c{c}}{\~a}o de 
                         T-Tuplas), para gerar casos de teste de software via designs 
                         combinatoriais, especificamente via a t{\'e}cnica de Matriz de 
                         Cobertura com N{\'{\i}}veis Variados (MCNV), foi desenvolvido. A 
                         ideia geral do TTR {\'e} derivar uma MCNV M por meio da 
                         cria{\c{c}}{\~a}o e realoca{\c{c}}{\~a}o de t-tuplas para a 
                         matriz M, considerando um par{\^a}metro chamado meta 
                         (\$\zeta\$). Dois experimentos controlados e um 
                         quasiexperimento foram realizados para comparar o TTR com outros 
                         quatro algoritmos/ferramentas bastante conhecidos que geram MCNVs. 
                         No primeiro experimento controlado, comparou-se duas perspectivas 
                         de custo considerando a vers{\~a}o 1.1 do algoritmo TTR: tamanho 
                         das suites de teste e tempo para gerar as suites de teste. 
                         Al{\'e}m disso, realizou-se uma an{\'a}lise de similaridade 
                         entre esses conjuntos. No segundo experimento controlado, foi 
                         considerada uma vers{\~a}o melhorada do algoritmo TTR, 
                         vers{\~a}o 1.2, e comparou-se com os mesmos quatro 
                         algoritmos/ferramentas anteriores, mas considerando somente a 
                         perspectiva de custo relacionada ao tamanho das suites de teste e 
                         an{\'a}lise de similaridade. Por fim, um quasiexperimento foi 
                         realizado onde comparou-se a efici{\^e}ncia entre o TTR 1.2 e as 
                         outras quatro solu{\c{c}}{\~o}es, usando an{\'a}lise de 
                         mutantes e aplicando a um estudo de caso da {\'a}rea espacial. As 
                         conclus{\~o}es dessas tr{\^e}s avalia{\c{c}}{\~o}es rigorosas 
                         s{\~a}o que o TTR foi o algoritmo que apresentou melhor custo 
                         (menor quantidade de casos de teste para serem executados), mas 
                         que n{\~a}o h{\'a} diferen{\c{c}}a de efici{\^e}ncia entre o 
                         TTR e as demais solu{\c{c}}{\~o}es. Al{\'e}m disso, as suites 
                         de teste n{\~a}o s{\~a}o similares, comparando o TTR com as 
                         outras solu{\c{c}}{\~o}es. Desse modo, pode-se afirmar que o TTR 
                         foi superior aos demais algoritmos/ferramentas pois teve mesma 
                         efici{\^e}ncia mas melhor custo. ABSTRACT: With respect to 
                         complex systems, such as software developed for space applications 
                         like satellites, stratospheric balloons and rockets, there are 
                         always risks related to product malfunctioning that can cause 
                         damage to the environment, great financial losses, or worse, loss 
                         of lives. To minimize these risks, the testing process of these 
                         systems must be rigorous and efficient. Since it is not possible 
                         to test such products exhaustively, given the wide range of 
                         possible test cases, it is critical, therefore, that there are 
                         available methods for the generation/selection of test cases which 
                         have great potential for defects detection and a reduced cost. In 
                         this direction, combinatorial designs have drawn attention of the 
                         software testing community to generate sets of smaller (lower cost 
                         to run) and efficient (ability to find software defects) test 
                         cases. Therefore, this master dissertation aims to present a new 
                         way for generating sets of test cases via combinatorial designs, 
                         where such test cases have a lower cost and efficiency comparable 
                         to solutions already existing in the literature. Then, an 
                         algorithm, called T-Tuple Reallocation (TTR), to generate software 
                         test cases via combinatorial designs, specifically via the 
                         Mixed-Level Covering Array technique (MCA) was developed. The main 
                         reasoning behind TTR is to derive an MCA M by creating and 
                         relocating t-tuples to the matrix M, considering a parameter 
                         called goal (\$\zeta\$). Two controlled experiments and one 
                         quasiexperiment were performed to compare TTR with four other 
                         well-known algorithms/tools that generate MCAs. In the first 
                         controlled experiment, version 1.1 of TTR was compared considering 
                         two cost perspectives: size of the test suites and time to 
                         generate the test suites. In addition, a similarity analysis was 
                         accomplished between these sets. In the second controlled 
                         experiment, an improved version of TTR, version 1.2, was compared 
                         with the same four previous algorithms/tools, but only in the cost 
                         perspective related to the size of the test suites and similarity 
                         analysis. Finally, a quasiexperiment aimed to assess the 
                         efficiency between TTR 1.2 and the other four solutions was 
                         carried out, via mutation testing and a space application case 
                         study. The conclusions of these three rigorous evaluations are 
                         that TTR was the algorithm that presented the better cost (smaller 
                         number of test cases to execute), but that there is no difference 
                         in efficiency between TTR and the other solutions. In addition, 
                         the test suites are not similar, comparing TTR with the other 
                         approaches. Thus, it can be asserted that the TTR algorithm was 
                         superior to the other algorithms/tools because it had the same 
                         efficiency but better cost.",
            committee = "Mendes, Celso Luiz (presidente) and Santiago J{\'u}nior, 
                         Valdivino Alexandre de (orientador) and Guerra, Eduardo Martins 
                         and Martins, Luiz Eduardo Galv{\~a}o",
         englishtitle = "An algorithm for the generation of combinatorial test cases via 
                         mixed-level covering arrays",
             language = "pt",
                pages = "109",
                  ibi = "8JMKD3MGP3W34P/3NK7TBE",
                  url = "http://urlib.net/rep/8JMKD3MGP3W34P/3NK7TBE",
           targetfile = "publicacao.pdf",
        urlaccessdate = "04 dez. 2020"
}


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