Automated test case generation for web APIs is a thriving research topic, where test cases are frequently derived from the API specification. However, this process is only partially automated since testers are usually obliged to manually set meaningful valid test inputs for each input parameter. In this article, we present ARTE, an approach for the automated extraction of realistic test data for web APIs from knowledge bases like DBpedia. Specifically, ARTE leverages the specification of the API parameters to automatically search for realistic test inputs using natural language processing, search-based, and knowledge extraction techniques. ARTE has been integrated into RESTest, an open-source testing framework for RESTful APIs, fully automating the test case generation process. Evaluation results on 140 operations from 48 real-world web APIs show that ARTE can efficiently generate realistic test inputs for 64.9% of the target parameters, outperforming the state-of-the-art approach SAIGEN (31.8%). More importantly, ARTE supported the generation of over twice as many valid API calls (57.3%) as random generation (20%) and SAIGEN (26%), leading to a higher failure detection capability and uncovering several real-world bugs. These results show the potential of ARTE for enhancing existing web API testing tools, achieving an unprecedented level of automation.

Nowadays, service trading over the Web is gaining momentum. In this highly dynamic scenario, both providers and consumers need to formalize their contractual and legal relationship, creating service level agreements. Although there exist some proposals that provide models to describe that relationship, they usually only cover technical aspects, not providing explicit semantics to the agreement terms. Furthermore, these models cannot be effectively shared on the Web, since they do not actually follow Web principles. These drawbacks hamper take-up and automatic analysis. In this article, we introduce Linked USDL Agreement, a semantic model to specify, manage and share service level agreement descriptions on the Web. This model is part of the Linked USDL family of ontologies that can describe not only technical but also business related aspects of services, incorporating Web principles. We validate our proposal by describing agreements in computational and non-computational scenarios, namely cloud computing and business process outsourcing services. Moreover, we evaluate the actual coverage and expressiveness of Linked USDL Agreement comparing it with existing models. In order to foster its adoption and effectively manage the service level agreement lifecycle, we present an implemented tool that supports creation, automatic analysis, and publication on the Web of agreement descriptions.

As the use of services available on the Web is becoming mainstream, contracts and legal aspects of the relationship between providers and consumers need to be formalized. However, current proposals to model service level agreements are mostly focused on technical aspects, do not explicitly provide semantics to agreement terms, and do not follow Web principles. These limitations prevent take-up, automatic processing, and effective sharing of agreements. Linked USDL Agreement is a Linked Data based semantic model to describe and share service agreements that extends Linked USDL, which offers a family of languages to describe various technical and business aspects of services. We followed a use case driven approach, evaluating the applicability of our proposal in a cloud computing scenario, and comparing its expressiveness with existing models. Finally, we show a concrete tool that helps to model and check the validity of agreements.

With the rapid development of the Web in the direction Social Media, the number of communication possibilities has increased exponentially, bringing new challenges and opportunities for companies to build and shape their reputation online as well as to engage and maintain good relations with their customers. In this paper we describe how semantic technologies enable scalable, effective and efficient on-line communication. We propose four different ways in which semantics can be used for this purpose. First, we discuss semantic analysis of communication items based on 'classical' semantics, such as natural language processing. Second, we look at semantics as a channel, viewing Linked Open Data vocabularies not only as terminological assets but as communication channels. Third, we demonstrate how semantics provide the methodologies and tools for content modeling by means of ontologies. Finally, we outline how semantics through semantic matchmaking enable semi-automatic assignment and distribution of content to channels and vice-versa.