Submitted by Marlene Santos (marlene.bc.ufg@gmail.com) on 2016-06-09T17:02:10Z No. of bitstreams: 2 Tese - Eduardo Noronha de Andrade Freitas - 2016.pdf: 1936673 bytes, checksum: 4336d187b0e552ae806ef83b9f695db0 (MD5) license_rdf: 19874 bytes, checksum: 38cb62ef53e6f513db2fb7e337df6485 (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2016-06-10T11:14:00Z (GMT) No. of bitstreams: 2 Tese - Eduardo Noronha de Andrade Freitas - 2016.pdf: 1936673 bytes, checksum: 4336d187b0e552ae806ef83b9f695db0 (MD5) license_rdf: 19874 bytes, checksum: 38cb62ef53e6f513db2fb7e337df6485 (MD5) / Made available in DSpace on 2016-06-10T11:14:00Z (GMT). No. of bitstreams: 2 Tese - Eduardo Noronha de Andrade Freitas - 2016.pdf: 1936673 bytes, checksum: 4336d187b0e552ae806ef83b9f695db0 (MD5) license_rdf: 19874 bytes, checksum: 38cb62ef53e6f513db2fb7e337df6485 (MD5) Previous issue date: 2016-02-15 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / Fundação de Amparo à Pesquisa do Estado de Goiás - FAPEG / The creation of a suite of unit testing is preceded by the selection of which components (code units) should be tested. This selection is a significant challenge, usually made based on the team member’s experience or guided by defect prediction or fault localization models. We modeled the selection of components for unit testing with limited resources as a multi-objective problem, addressing two different objectives: maximizing benefits and minimizing cost. To measure the benefit of a component, we made use of important metrics from static analysis (cost of future maintenance), dynamic analysis (risk of fault, and frequency of calls), and business value. We tackled gaps and challenges in the literature to formulate an effective method, the Selector of Software Components for Unit testing (SCOUT). SCOUT was structured in two stages: an automated extraction of all necessary data and a multi-objective optimization process. The Android platform was chosen to perform our experiments, and nine leading open-source applications were used as our subjects. SCOUT was compared with two of the most frequently used strategies in terms of efficacy.We also compared the effectiveness and efficiency of seven algorithms in solving a multi-objective component selection problem: random technique; constructivist heuristic; Gurobi, a commercial tool; genetic algorithm; SPEA_II; NSGA_II; and NSGA_III. The results indicate the benefits of using multi-objective evolutionary approaches such as NSGA_II and demonstrate that SCOUT has a significant potential to reduce market vulnerability. To the best of our knowledge, SCOUT is the first method to assist software testing managers in selecting components at the method level for the development of unit testing in an automated way based on a multi-objective approach, exploring static and dynamic metrics and business value. / (Sem resumo)

Submitted by Jaqueline Silva (jtas29@gmail.com) on 2014-10-06T17:24:11Z No. of bitstreams: 2 Dissertação - André Assis Lôbo de Oliveira- 2013.pdf: 3915731 bytes, checksum: 2447fa437e5dca74e295727bd8fed4d1 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Approved for entry into archive by Jaqueline Silva (jtas29@gmail.com) on 2014-10-06T19:18:43Z (GMT) No. of bitstreams: 2 Dissertação - André Assis Lôbo de Oliveira- 2013.pdf: 3915731 bytes, checksum: 2447fa437e5dca74e295727bd8fed4d1 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Made available in DSpace on 2014-10-06T19:18:43Z (GMT). No. of bitstreams: 2 Dissertação - André Assis Lôbo de Oliveira- 2013.pdf: 3915731 bytes, checksum: 2447fa437e5dca74e295727bd8fed4d1 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Previous issue date: 2013-12-05 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / Verification and Validation Activities (V&V) consume about 50% to 60% of the total cost of a software lifecycle. Among those activities, Software Testing technique is one which is mostly used during this process. One of the main problems related to detected in Software Testing is to find a set of tests (subset from input domain of the problem) which is effective to detect the remaining bugs in the software. The Search-Based Software Testing (SBST) approach uses metaheuristics to find low cost set of tests with a high effectiveness to detect bugs. From several existing test criteria, Mutation Testing is considered quite promising to reveal bugs, despite its high computational cost, due to the great quantity of mutant programs generated. Therefore, this dissertation addresses the problem of selecting mutant programs and test cases in Mutation Testing context. To this end, it is proposed a Coevolutionary Genetic Algorithm (CGA) and the concept of Genetic Effectiveness, implemented by Genetic Classification (GC) and new genetic operators adapted to the proposed representation. Furthermore, the Genetic Algorithm Coevolutionary with Controlled Genetic Classification (CGA􀀀CGCop) is proposed for improving the efficiency of CGA’s GC. The CGA is applied in three categories of benchmarks and compared to other five methods. The results show a better performance of the CGA in subsets selection with better mutation score, as well as improvement of CGA􀀀CGCop in use of GC. These results evidence the proposal approach with promising use in the context of Mutation Testing. / Atividades de Validação e Verificação (V&V) consomem cerca de 50% a 60% do custo total no ciclo de vida de um software. Dentre essas, o Teste de Software é uma das atividades mais empregadas. Um dos maiores problemas do Teste de Software é encontrar um conjunto de teste (subconjunto do domínio de entrada do problema) que seja eficaz em detectar os defeitos remanescentes no software. Neste contexto, a Search-Based Software Testing (SBST) é uma linha de pesquisa recente que vem propondo boas soluções, uma vez que utiliza-se de metaheurísticas para encontrar um conjunto de teste com baixo custo e grande eficácia na detecção de defeitos. Dentre os diversos critérios de teste existentes, o Teste de Mutação é bastante promissor na revelação de defeitos, entretanto apresenta um alto custo computacional em termos de aplicabilidade. Por isso, a pesquisa aborda o problema de seleção de programas mutantes e casos de teste no contexto do Teste de Mutação. Para tal, propõe o Algoritmo Genético Coevolucionário (AGC) que traz o conceito de Efetividade Genética, implementado pela Classificação Genética (CG) e por novos operadores genéticos adaptados à representação proposta. Além disso, propõe o Algoritmo Genético Coevolucionário com Classificação Genética Controlada (AGC 􀀀CGCop) para a melhoria da eficiência da CG do AGC. O algoritmo AGC é aplicado em três classes de benchmarks e comparado com outros cinco métodos. Os resultados demonstram um melhor desempenho do AGC na seleção de subconjuntos com melhor escore de mutação, bem como um aprimoramento do AGC􀀀CGCop no uso da CG. Tais resultados evidenciam a abordagem proposta com uso promissor no contexto do Teste de Mutação.

Submitted by Luciana Ferreira (lucgeral@gmail.com) on 2016-10-04T12:46:23Z No. of bitstreams: 2 Dissertação - Bruno Nunes Machado - 2016.pdf: 954291 bytes, checksum: 2b4b0a80a709d8803e7d0857e9aad0dd (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2016-10-04T12:46:59Z (GMT) No. of bitstreams: 2 Dissertação - Bruno Nunes Machado - 2016.pdf: 954291 bytes, checksum: 2b4b0a80a709d8803e7d0857e9aad0dd (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2016-10-04T12:46:59Z (GMT). No. of bitstreams: 2 Dissertação - Bruno Nunes Machado - 2016.pdf: 954291 bytes, checksum: 2b4b0a80a709d8803e7d0857e9aad0dd (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2016-09-01 / Fundação de Amparo à Pesquisa do Estado de Goiás - FAPEG / The software testing is an important component of software development life cycle, that directly affects quality of software products. Some problems in software testing phase can not be optimized only with traditional Software Engineering techniques. It is possible to do the mathematical modelling of those problems in an attempt to optimize them through the search techniques. However, the use of optimization approaches tend to incorporate more and more activities decisions to the tester, making more complex test activity. So, in order that optimization techniques are in fact employed at the Software Test solutions, the ability to abstract the details of optimization are required. Thus, the objective of this research is to propose a framework for search-based software testing (SBST). The proposed framework works as a top-level layer over generic optimization frameworks and testing software tools, it's target is supporting software testers that are not able to use optimization frameworks during a testing activity due to short deadlines and limited resources or skills, also supporting expert or beginners users from optimization area that need or want to compare their metaheuristics with ones from literature and offered by the proposed framework. The framework was evaluated in a case study of software testing scenario. This scenario was modeled as test case selection problem in which experiments were executed with different metaheuristics and benchmarks offered by framework. The results indicate it's capability to support the SBST area with emphasis on the test cases selection. The framework was evaluated and compared with other SBST frameworks in terms of quality metrics, that indicated its extensibility and flexibility as framework. / O Teste de Software é uma parte essencial do processo de desenvolvimento de software, com impacto direto na qualidade do produto de software. Alguns problemas detectados durante a fase de teste de software não são possíveis de serem resolvidos apenas com as técnicas tradicionais da Engenharia de Software. Nestes casos é possível realizar a modelagem matemática desses problemas e tentar otimizá-los por meio das técnicas de busca. Entretanto, a utilização de abordagens de otimização tende a incorporar mais decisões e mais atividades para o testador, tornando a atividade de teste mais complexa. Assim, para que as técnicas de otimização sejam de fato empregadas no Teste de Software, soluções com a capacidade de abstrair detalhes da otimização são necessárias. Diante disso, o objetivo desta pesquisa consiste em propor um framework para apoiar o Teste de Software Baseado em Busca. O framework proposto funciona como uma camada de alto nível sobre os frameworks genéricos de otimização e as ferramentas de teste de software, apoiando testadores de software que não são capazes de utilizar os frameworks de otimização durante uma atividade de teste devido a prazos curtos e recursos ou habilidades limitadas, além de apoiar usuários iniciantes ou especialistas da área de otimização que precisam ou desejam comparar suas metaheurísticas ou heurísticas com as da literatura e as oferecidas pelo framework proposto. O framework foi avaliado em um estudo de caso no cenário de teste de software. Tal cenário foi modelo como um problema de seleção de casos de teste, em que experimentos foram executados com diferentes metaheurísticas e benchmarks oferecidos pelo framework. Os resultados indicaram a capacidade do framework em apoiar a aréa de SBST, com destaque para o problema de seleção de casos de teste. Além disso, o framework também foi avaliado e comparado com outro framework SBST em termos de métricas de qualidade, que indicaram a extensibilidade e flexibilidade do framework proposto.