Domain-driven design (DDD) is commonly used to design microservices. A crucial aspect of microservice design is API design, which includes the design of API endpoints. Our objective is to automate the assessment of conformance to Architectural Design Decisions (ADDs) on the interrelation of DDD and APIs. In particular, we studied link mapping, API operation design, and resource segregation as API endpoint design issues that are linked to domain model design. We particularly aim to address conformance checking in the context of frequent release practices, as frequent manual conformance checking is difficult or infeasible. We suggest a new approach for the automated assessment of conformance to ADD options. The approach suggests automated detectors to detect ADD options selected in a given API endpoint design, as well as an assessment scoring scheme based on empirical results. For the evaluation of our approach, we first manually created a ground truth for 12 cases in a multi-case study, and then compared the results of our automated detectors to the ground truth for each of those cases. With our approach, all ADD options in our multi-case study possibly can be automatically detected. Without further improvements, our approach identifies 83% of the decision points in the multi-case study correctly. A statistical analysis of our data shows only a negligible effect size for differences to the ground truth. Our new approach provides a pragmatic method for automated detection of conformance to ADDs on the interrelation of DDD and APIs. The approach can support the continuous analysis of API endpoint designs.

Microservice API design is a critical aspect in crafting a microservice architecture. While API design in general has been studied, the specific relation of API design to design practices and models commonly used in microservice architectures is yet understudied. In particular, practitioners frequently use Domain-Driven Design (DDD) in their microservice architecture and API designs. We thus decided to study existing Architectural Design Decisions (ADDs), their solutions options, their relations, and the decision drivers in these decisions. Using the Grounded Theory research method we studied grey literature sources. In this study, we identified six ADDs with 27 decision options, numerous relations between them, and 27 decision drivers. The decisions cover mapping domain models to APIs, defining API contracts in relation to domain models, designing API resources based on domain model elements, segregation of API resources, mapping domain model links to the API, and designing the operations of an API resource.

Code and design smells, such as the coupling smells examined in this article, are widely studied. Existing empirical studies reveal gaps between the scientific theory and practice, not yet explained by the scientific literature. Only basic coupling smell detection approaches and metrics seem to have been transferred to practice so far. This article aims to study the current practitioner’s understanding of coupling smells. Based on grey literature sources containing practitioner views on coupling smells, we performed a Grounded Theory (GT) study. We used UML-based modeling to precisely encode our findings and performed a rigorous analysis of our codes and models. Our results are defining factors of coupling smells, as well as smell impacts, trade-offs, relationships to other smells, relationships to practices and patterns, and fix options as perceived by practitioners. We further identified gaps in the understanding of coupling smells between science and practice, and derived opportunities and challenges for future scientific work. Five lessons are presented as opportunities and challenges for future research. Our results can help scientists to get a better understanding of practitioner concerns, and practitioners to get an overview of the current perception of other practitioners on coupling smells.