Uma abordagem estruturada para observabilidade em microsserviços: taxonomia, catálogo e framework de detecção de anti-padrões

Abstract

Software systems increasingly rely on microservices-based architectures to enhance scalability, modularity, and continuous deployment. Although this approach simplifies development by promoting a functional decomposition of components, it also introduces significant operational complexity, making failures more frequent and harder to diagnose. In this context, observability emerges as a fundamental concept, defined as the ability to understand and diagnose the internal behavior of a system based on its external outputs, such as metrics, logs, and traces. However, despite its importance, observability is often poorly implemented due to the lack of standardized practices, resulting in ineffective monitoring, alert fatigue, and low efficiency in incident response. Although existing studies discuss concepts, tools, and challenges related to observability, no prior work has focused specifically on observability anti-patterns, recurrent practices that undermine monitoring effectiveness, nor proposed solutions capable of detecting them automatically. Furthermore, there is a lack of a comprehensive taxonomy to classify and organize existing studies on observability. To address these gaps, this thesis presents a taxonomy focused on observability in microservices-based applications, constructed through a systematic mapping of the literature. A total of 84 relevant studies published between 2019 and 2025 were analyzed, providing a comprehensive overview of the field. The review also identifies tools, benchmarking applications, and real-world datasets used in the selected studies. Complementing this contribution, the thesis develops a systematized catalog of observability anti-patterns, offering an approach to identify and mitigate harmful practices. This catalog serves as a practical guide to support teams in building more reliable and efficient systems. In total, 37 anti-patterns were identified, whose relevance was evaluated by 60 experts, achieving an agreement rate of 95%. Finally, the thesis introduces the Observa framework, designed to automatically detect observability anti-patterns. Its operation was evaluated through three experiments and a proof of concept, which demonstrated its technical feasibility and approved its adoption, receiving a recommendation of excellence from the evaluators.