Search for: All records

Microservices have gained widespread adoption in enterprise software systems because they encapsulate the expertise of specific organizational subunits. This approach offers valuable insights into internal processes and communication channels. The advantage of microservices lies in their self-contained nature, streamlining management and deployment. However, this decentralized approach scatters knowledge across microservices, making it challenging to grasp the holistic system. As these systems continually evolve, substantial changes may affect not only individual microservices but the entire system. This dynamic environment increases the complexity of system maintenance, emphasizing the need for centralized assessment methods to analyze these changes. This paper derives and introduces quantification metrics to serve as indicators for investigating system architecture evolution across different system versions. It focuses on two holistic viewpoints of inter-service interaction and data perspectives derived through static analysis of the system’s source code. The approach is demonstrated with a case study using established microservice system benchmarks. 

Test coverage is a critical aspect of the software development process, aiming for overall confidence in the product. When considering cloud-native systems, testing becomes complex, as it becomes necessary to deal with multiple distributed microservices that are developed by different teams and may change quite rapidly. In such a dynamic environment, it is important to track test coverage. This is especially relevant for end-to-end (E2E) and API testing, as these might be developed by teams distinct from microservice developers. Moreover, indirection exists in E2E, where the testers may see the user interface but not know how comprehensive the test suits are. To ensure confidence in health checks in the system, mechanisms and instruments are needed to indicate the test coverage level. Unfortunately, there is a lack of such mechanisms for cloud-native systems. This manuscript introduces test coverage metrics for evaluating the extent of E2E and API test suite coverage for microservice endpoints. It elaborates on automating the calculation of these metrics with access to microservice codebases and system testing traces, delves into the process, and offers feedback with a visual perspective, emphasizing test coverage across microservices. To demonstrate the viability of the proposed approach, we implement a proof-of-concept tool and perform a case study on a well-established system benchmark assessing existing E2E and API test suites with regard to test coverage using the proposed endpoint metrics. The results of endpoint coverage reflect the diverse perspectives of both testing approaches. API testing achieved 91.98% coverage in the benchmark, whereas E2E testing achieved 45.42%. Combining both coverage results yielded a slight increase to approximately 92.36%, attributed to a few endpoints tested exclusively through one testing approach, not covered by the other. 

Internet of Things (IoT) devices have been widely adopted in recent years. Unlike conventional information systems, IoT solutions have greater access to real-world contextual data and are typically deployed in an environment that cannot be fully controlled, and these circumstances create new challenges and opportunities. In this article, we leverage the knowledge that an IoT device has about its network context to provide an additional security factor. The device periodically scans a network and reports a list of all devices in the network. The server analyzes movements in the network and subsequently reacts to suspicious events. This article describes how our method can detect network changes, retrieved only from scanning devices in the network. To demonstrate the proposed solution, we perform a multi-week case study on a network with hundreds of active devices and confirm that our method can detect network anomalies or changes.