One of the chief problems in software architecture is avoiding architecture model drift and erosion in all kinds of complex software systems. Microservice-based systems introduce new challenges in this context, as they often use a large variety of technologies in their latest iteration, and are changed and released very frequently. Existing solutions that can be used to reconstruct architecture models fall short in addressing these new challenges, as they cannot easily cope with continuous evolution, their accuracy is too low, and highly polyglot settings are not supported well. In this work, we report on a research study aiming to design a highly accurate architecture model abstraction approach for comprehending component architecture models of highly polyglot systems that can cope with continuous evolution. After analyzing the results of related studies, we found two possible architecture model abstraction approaches that meet the requirements of our study: an opportunistic, and a reusable semi-automatic detector-based approach. We have conducted an empirical case study for validation and comparison of the two approaches. We conclude that both detector approaches are feasible. In our case study, the reusable approach breaks even in terms of time and effort needed for establishing reuse, if modest reuse of detectors is possible, and is producing slightly more high quality and evolution-stable solutions than the opportunistic approach.

Isolation, autonomy, and loose coupling are critical success factors of microservice architectures. Unfortunately, systems tend to become strongly coupled over time, sometimes even exhibiting cyclic communication chains, making the individual deployment of services challenging. Such chains are highly problematic when strongly coupled communication e.g. based on synchronous invocations is used, but also create complexity and maintenance issues in more loosely coupled asynchronous or event-based communication. Here, cycles only manifest on a conceptual or domain level, making them hard to track for algorithms that rely solely on static analysis. Accordingly, previous attempts to detect cycles either focused on synchronous communication or had to collect additional runtime data, which can be costly and time-consuming. We suggest a novel approach for identifying and evaluating domain-based cyclic dependencies in microservice systems based on modular, reusable source code detectors. Based on the architecture model reconstructed by the detectors, we derived a set of architectural metrics for detecting and classifying domain-based cyclical dependencies. By conducting two case studies on open-source microservice architectures, we validated the feasibility and applicability of our approach.

Data transfer and exchange of information through APIs are essential for each microservice architecture. Since these transfers often include private or sensitive data, potential data leaks, either accidentally or through malicious attacks, provide a high-security risk. While there are different techniques, like using data encryption or authentication protocols to secure the data exchange, only a few strategies are known to reduce the damage when an actual data breach happens. Our work presents a novel approach to identifying the optimal amount of data attributes that need to be exchanged between APIs and minimizes the damage in case of a potential breach. Our method relies only on static source code analysis and easy-to-calculate architectural metrics, making it well suited to be used in continuous integration and deployment processes. We further verified and validated the feasibility of our approach by conducting two case studies on open-source microservice systems.

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