Deprecation is a way to inform clients using an application programming interface (API) that the usage of this API is discouraged. Tool support and research for deprecation in local APIs are well established. However, nowadays web APIs are more commonly used, e.g., using the REST architectural style. However, the techniques to detect and handle the usage of deprecated local APIs cannot be directly applied to web APIs. Previous approaches for detecting deprecated web APIs focus on static analysis of client code by detecting calls to web APIs and, subsequently, an investigation of associated API specifications. These approaches currently have two essential limitations: (i) The target of an API call can often not be determined statically. (ii) Deprecation in API specifications is not the only way to signal deprecation for web APIs. We introduce a dynamic approach using tracing to detect calls to web APIs. Subsequently, we check the called APIs for deprecation using an API specification, response meta-data, or a knowledge base. This approach addresses both limitations of the detection with static analysis. We implement the approach and evaluate it on three projects, including client-server calls as well as a microservice benchmark system. The empirical evaluation yields a precision of 1.00 and a recall of 0.95. The false negatives can be attributed to a shortcoming in the automatic instrumentation provided by OpenTelemetry observability framework.

Application Programming Interfaces (APIs) are the prevalent interaction method for software modules, components, and systems. As systems and APIs evolve, an API element may be marked as deprecated, indicating that its use is disapproved or that the feature will be removed in an upcoming version. Consequently, deprecation is a means of communication between developers and, ideally, complemented by further documentation, including suggestions for the developers of the API’s clients.API deprecation is a relatively young research area that recently gained traction among researchers. To identify the current state of research as well as to identify open research areas, a meta-study that assesses scientific studies is necessary. Therefore, this paper presents a systematic mapping study on API deprecation to classify the state of the art and identify gaps in the research field. We identified and mapped 36 primary studies into a classification scheme comprising general and API-specific categories.We identified five major gaps in previous research on API deprecation as opportunities for future studies: studying remote APIs, investigating a broader range of static APIs, joining suppliers’ and clients’ views, including humans in studies, and avoiding deprecation.

The usage of large amounts of data has an immense potential for global economic growth and the competitiveness of countries with high technological standards. Vast amounts of data from different sources are collected and analyzed in order to seek economic profit and competitive advantages for companies and society in general. To gain profit from such data, it needs to be analyzed, processed, and interpreted. Thus, knowledge can be created and such generation of knowledge within the analysis and interpretation process constitutes the difference between "Big" and "Smart" Data. In this paper we present a taxonomy to develop standards in the field of Smart Data. It consists of 8 challenges that need to be addressed by standards and 13 fields of standardization.