Understanding the relationship between continuous integration and its effects on software quality outcomes

• Ano de defesa: 2020
• Autor(a) principal: Nery, Gustavo Sizílio
• Orientador(a): Kulesza, Uirá
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Rio Grande do Norte
• Programa de Pós-Graduação: PROGRAMA DE PÓS-GRADUAÇÃO EM SISTEMAS E COMPUTAÇÃO
• País: Brasil
• Palavras-chave em Português: Continuous integration; Software quality; Empirical study; Causal inference
• Área do conhecimento CNPq: CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::SISTEMAS DE COMPUTACAO
• Link de acesso: https://repositorio.ufrn.br/jspui/handle/123456789/29655

Continuous Integration (CI) is the practice of automating and improving the frequency of code integration (e.g., daily builds). CI is often considered one of the key elements involved to support agile software teams. It helps to reduce the risks in software development by automatically building and testing a project codebase, which allows the team to fix broken builds immediately. The adoption of CI can help development teams to assess the quality of software systems. The potential benefits of adopting CI have brought the attention of researchers to study its advantages empirically. Previous research has studied the impact of adopting CI on diverse aspects of software development. Despite the valuable advancements, there are still many assumptions in the community that remains empirically unexplored. Our work empirically investigates the software quality outcomes and their relationship with the adoption of CI. This thesis provides a systematic literature mapping that presents a broad knowledge of how practitioners and researchers recognize the CI practice to affect software product-related aspects. Additionally, we improve some assumptions by performing two empirical studies that aim to answer the following open questions: (i) Does the adoption of CI share a relationship with the evolution of test code? (ii) The adherence to CI best practices is related to the degree of code quality? Finally, we present a pioneering study that goes beyond the correlation tests to investigate the estimated causal effect of CI adoption and its impact on automated tests. Thereby, we apply a causal inference using directed acyclic graphs and probabilistic methods to determine the causal effect of CI in automated tests. Our results suggest that, despite the CI adoption trade-offs, it is likely to be associated with improvements in software quality. Additionally, it employs a considerable positive causal effect on the volume of automated tests.