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To rapidly improve the quality of web applications, it is necessary to implement automated testing strategies that allow you to comprehensively evaluate functionality and performance attributes, which are directly affected by the behavior of the hardware of the servers where they are hosted. Just as IT teams monitor the infrastructure that supports the applications, automations should be supported by monitoring platforms with specialized software testing analytics. This article presents the evaluation of four technology stacks, which will make it possible to select the most appropriate one to be included in the development of a platform specialized in software quality analytics. The four stacks in this study are: PALG, TICK, ELK, AM. This article will explain the characteristics and differences of stacks compared to various components such as: database, information filtering, collection of operating system metrics, alert system, data ingestion and visualization. Then simulations and tests carried out on each of them are described to evaluate their capabilities. Finally, conclusions are issued that will allow to select the technology stack that will be used in the development of the system specialized analytics for software testing. [ABSTRACT FROM AUTHOR]

Information retrieval (IR) deals with representation, organization, and storage of information. IR should provide the user with an easy access to the information in which he or she is interested. However, characterization of the user information needs is a complex problem. Usually, a full description of the user information needs cannot be made directly and a set of keywords should rather be used. Thus, given a set of keywords, an IR system retrieves information, which may be relevant to the user. However, traditional IR systems overload recovery information. Personalization systems, in particular, the recommender ones, have been proposed in order to mitigate the impact of an enormous amount of retrieved information on the user. This paper describes the architecture of PREDICA, an experimental platform for combining advanced query and recommender systems, adaptive interface, data warehouse and knowledge discovery on database in a digital library. PREDICA uses a pre-processing strategy for improving the search of information and personalization strategies for documents' recommendation. Among other open source digital library research projects, like D-Space, Eprints, and Greenstone, PREDICA has an integrated recommender system to provide helpful information to the users. [ABSTRACT FROM AUTHOR]