Are REST APIs for Cloud Computing Well-Designed? An Exploratory Study

Abstract

Abstract. Cloud computing is currently the most popular model to offer and access computational resources and services. Many cloud providers use the REST architectural style (Representational State Transfer) for offering such computational resources. However, these cloud providers face challenges when designing and exposing REST APIs that are easy to handle by end-users and/or developers. Yet, they benefit from best practices to help them design understandable and reusable REST APIs. However, these best practices are scattered in the literature and they have not be studied systematically on real-world APIs. Consequently, we propose two contributions. In our first contribution, we survey the literature and compile a catalog of 73 best practices in the design of REST APIs making APIs more understandable and reusable. In our second contribution, we perform a study of three different and well-known REST APIs from three cloud providers to investigate how their APIs are offered and accessed. These cloud providers are Google Cloud Platform, OpenStack, and Open Cloud Computing Interface (OCCI). In particular, we evaluate the coverage of the features provided by the REST APIs of these cloud providers and their conformance with the best practices for REST APIs design. Our results show that Google Cloud follows 66% (48/73), OpenStack follows 62% (45/73), and OCCI 1.2 follows 56% (41/73) of the best practices. Second, although these numbers are not necessarily high, partly because of the strict and precise specification of best practices, we showed that cloud APIs reach an acceptable level of maturity.

A systematic literature review on the detection of smells and their evolution in object‐oriented and service‐oriented systems

“…113 29,35,62 or an SLR to check the most relevant information presented for either detection of code smells 26,27,72 or correction of code smells 109 or code decay. 52 Empirical source code analysis mostly investigates the cause-effect relationship like smell versus maintenance effort, 53,91 best practices of services in an open-source platform, 77 class performance, 68 change proneness of antipatterns and clones, 73 refactoring suggestions 58,67,68 after smell detection, or statistical techniques to check the relationship between already presented smell and design patterns. 69 Table 9 summarizes the extracted information of 23 studies that rely on the empirical source code analysis techniques.…”

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