Efficient Event Aggregation using Eiffel Protocol in CI/CD

Background: In today’s software development, the systems that manage and automate how new software is built and delivered, known as Continuous Integration and Continuous Delivery (CI/CD) pipelines, are becoming more complex. These systems need to deal with many different events that happen during the software development process. Traditional tools used for organizing and understanding these events, like Ericsson’s Eiffel Intelligence tool, often struggle with complicated scenarios and are not easy to use. This makes it hard for developers to get a clearpicture of the software’s development stages, which is crucial for making timely andinformed decisions. Therefore, there’s a strong need for better tools that can handle these challenges more effectively. Objectives: This thesis aims to introduce an alternative event aggregation solutionfor CI/CD pipelines by addressing the limitations of Eiffel Intelligence. The objectives include: Identifying existing event aggregation methods. Analyzingtheir performance and efficiency within CI/CD processes. Implementing a newaggregation model using Apache Kafka and Apache Flink. Comparing the newmodel against Eiffel Intelligence in terms of efficiency, usability, and complexity. Methods: A comprehensive literature review was conducted to evaluate current event aggregation tools. An experimental setup involving Apache Kafka, and ApacheFlink, was implemented to simulate real-time event aggregation. Data from CI/CD pipelines were processed using this setup, and performance metrics such as code complexity and system scalability were measured and analyzed. Results: The new event aggregation tool we created using Java addressed a criticalissue that the existing Eiffel Intelligence tool could not solve, specifically a complex fourth use case that had previously been left unmanaged. This improvement significantly boosts the tool’s ability to handle challenging scenarios within CI/CD pipelines. Our solution makes rule-writing simpler by using straight forward coding methods instead of complex rules, leading to a cleaner and easier-to-understand codebase. The tool successfully processes event data in real time, making it more user-friendly and adaptable for managing CI/CD operations effectively. Conclusion: This thesis presents an effective alternative to the existing Eiffel Intelligence tool, demonstrating that the integration of Apache Kafka and ApacheFlink can enhance event aggregation processes within CI/CD pipelines. The modular design improves the flexibility, scalability, and usability of event handling. This approach offers valuable insights for organizations seeking to streamline their CI/CD processes, particularly in handling large-scale event data.