Detalhes bibliográficos
Ano de defesa: |
2020 |
Autor(a) principal: |
Costa , Júlio César de Lima
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Orientador(a): |
Castro, Leandro Nunes de
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Banca de defesa: |
Não Informado pela instituição |
Tipo de documento: |
Dissertação
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Tipo de acesso: |
Acesso aberto |
Idioma: |
por |
Instituição de defesa: |
Universidade Presbiteriana Mackenzie
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Programa de Pós-Graduação: |
Não Informado pela instituição
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Departamento: |
Não Informado pela instituição
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País: |
Não Informado pela instituição
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Palavras-chave em Português: |
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Área do conhecimento CNPq: |
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Link de acesso: |
https://dspace.mackenzie.br/handle/10899/28610
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Resumo: |
The constant search for code integration more and more frequently exposes the need to use automated mechanisms for compiling, running tests, packaging and delivering systems, bringing confidence to those who build and maintain software, as it provides quick feedback on to changes made. Software Engineering describes this process as a build. Because it is a process configured by humans and often also dependent on external services, machine resources and connectivity, they end up becoming sensitive, making some results non-deterministic, a factor that reduces confidence in feedback and increases the need for human research effort. Other areas also suffer loss, such as machine resources and the tomaket team. Given this problem, several studies that use the combination of Software Engineering and Data Science seek to identify non-deterministic results through intelligent algorithms, such as machine learning, thus reducing waste. However, the manual work of labeling builds to be used as input for training steps of the algorithms is quite costly see some cases, even unfeasible. This work proposes a heuristic of collection and automatic labeling of builds with a non-deterministic result and proposes the evolution of the heuristic through the use of clustering algorithms, improving its accuracy by 27%. The results of the labeling of builds with non-deterministic results in two audiences and open source reached a mark of 70% and 100% of accuracy in repositories with 650 and 1224 builds, respectively. |