Mutação de transformações para teste de programas Spark
The growth in the volume of data generated in the last years, a phenomenon known as Big Data, presented a series of challenges for its collection, storage and, especially, processing because they require important computational resources and adapted execution environments. Different parallel and dis...
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| Formato: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2020 |
| País: | Brasil |
| Recursos: | Universidade Federal do Rio Grande do Norte (UFRN) |
| Repositorio: | Repositório Institucional da UFRN |
| Idioma: | portugués |
| OAI Identifier: | oai:repositorio.ufrn.br:123456789/30646 |
| Acesso em linha: | https://repositorio.ufrn.br/handle/123456789/30646 |
| Access Level: | acceso abierto |
| Palavra-chave: | Big Data; Teste de Mutação; Apache Spark; Taxonomia; Operadores de Mutação. Mutation Operators Mutation Testing Transmut Spark |
| Resumo: | The growth in the volume of data generated in the last years, a phenomenon known as Big Data, presented a series of challenges for its collection, storage and, especially, processing because they require important computational resources and adapted execution environments. Different parallel and distributed processing systems are used for Big Data processing. Some systems adopt a control flow model, such as Hadoop, that applies the MapReduce programming style, while others adopt a data flow model, such as Apache Spark. The reliability of large-scale data processing programs becomes important due to the large amount of computational resources required for their execution, making it important to test them before they run in production in an expensive distributed computing infrastructure. This thesis proposes a mutation testing approach for programs that follow a data flow model like Apache Spark. Mutation testing is a testing technique that relies on simulating faults by modifying a program to create faulty versions called mutants. The generation of mutants is carried by mutation operators that are able to simulate specific faults in the program. Mutants are used in the test design and evaluation process in order to have a test set capable of identifying the faults simulated by the mutants. In order to apply the mutation testing process to Big Data processing programs, it is important to be aware of the types of faults that can be found in this context to design mutation operators that can simulate them. Thus, we conducted a study to characterize faults and problems that can appear in Spark programs. Based on this study, we designed a set of mutation operators for programs that follow a data flow model. These operators simulate faults in the program through changes in its data flow and operations. The mutation operators were formalized with a model we propose to represent data processing programs based on data flow. To support the application of our mutation operators, we developed the tool TRANSMUT-Spark that automates the main steps of the mutation testing process in Spark programs. We conducted experiments to evaluate the mutation operators and tool in terms of costs and effectiveness. The results of these experiments showed the feasibility of applying the mutation testing process in Spark programs and their contribution to the testing process in order to develop more reliable programs |
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