Web services are black box dependency magnets. Hence, studying how they evolve is both important and challenging. In this paper, we focus on one of the most successful stories of the serviceoriented paradigm in industry, i.e., the Amazon services. We perform a principled empirical study, that detects evolution patterns and regularities, based on Lehman's laws of software evolution. Our findings indicate that service evolution comes with spikes of change, followed by calm periods where the service is internally enhanced. Although spikes come with unpredictable volume, developers can count in the near certainty of the calm periods following them to allow their absorption. As deletions rarely occur, both the complexity and the exported functionality of a service increase over time (in fact, predictably). Based on the above findings, we provide recommendations that can be used by the developers of Web service applications for service selection and application maintenance.

Despite the particular standards, technologies, and trends (W3C, RESTful, microservices, etc.) that a team decides to follow for the development of a service‐oriented system, most likely the team members will have to use one or more services that solve general‐purpose problems like cloud computing, networking and content delivery, storage and database, management and governance, and application integration. Typically, general‐purpose services are long‐lived, they have several responsibilities, their interfaces are complex, and they grow over time. The way that these services evolve also affects the evolution of any system that will depend on them. Consequently, the selection of the particular services that will be used is a main concern for the team. In this paper, we report a pattern, called the athletic heart syndrome, which facilitates the selection of services that evolve properly. Patterns specify best practices that emerge from multiple real‐world cases. In our context, the athletic heart syndrome comes out from a study that concerns the evolution of a set of popular, long‐lived Amazon services that cover different domains. According to the athletic heart syndrome, the developers should select services whose heartbeat of changes looks like the heartbeat of an athlete when he is at rest. Specifically, the heartbeat of changes should consist mostly of calm periods, interrupted by few spikes of change. Similarly, the incremental growth of the services should involve mainly calm periods of maintenance, separated by spikes of growth. Selecting services that adhere to the pattern signifies high chances that the services evolve to deal with changing requirements. The pattern further guarantees that the service evolution involves both the expansion of the services with new functionalities and the maintenance of existing ones. The pattern also assures that the complexity increase in the service interfaces will be smooth and tolerable. Finally, conformance with the pattern implies that the growth of the services will be predictable.

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