Quality of an open source software ecosystem (OSS ecosystem) is key for different ecosystem actors such as contributors or adopters. In fact, the consideration of several quality aspects(e.g., activeness, visibility, interrelatedness, etc.) as a whole may provide a measure of the healthiness of OSS ecosystems. The more health a OSS ecosystem is, the more and better contributors and adopters it will gather. Some research tools have been developed to gather specific quality information from open source community data sources. However, there exist no frameworks available that can be used to evaluate their quality as a whole in order to obtain the health of an OSS ecosystem. To assess the health of these ecosystems, we propose to adopt robust principles and methods from the Service Oriented Computing field.
Autores: Oscar Franco-Bedoya / Marc Oriol / Carlos Müller / Jordi Marco / Pablo Fernández / Manuel Resinas / Xavier Franch / Antonio Ruiz-Cortés /
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Context: Modern services and applications need to react to changes in their context (e.g. location, memory consumption, number of users) to improve the user+IBk-s experience. To obtain this context, a monitoring infrastructure with adequate functionality and quality levels is required. But this monitoring infrastructure needs to react to the context as well, raising the need for context-aware monitoring tools. Objective: Provide a generic solution for context-aware monitoringable to effectively react to contextual changes. Method: We have designed CAMA, a service-oriented Context-Aware Monitoring Architecture that can be easily configured, adapted and evolved according to contextual changes. CAMA implements a decoupled architecture and manages a context domain ontology for modelling the inputs, outputs and capabilities of monitoring tools. Results: CAMA has been demonstrated in three real use cases. We have also conducted different evaluations, including an empirical study. The results of the evaluations show that (1) the overhead introduced by the architecture does not degrade the behavior of the system, except in extreme conditions; (2) the use of ontologies is not an impediment for practitioners, even when they have little knowledge about this concept; and (3) the reasoning capabilities of CAMA enable context-aware adaptations. CAMA is a solution useful for both researchers and practitioners. Researchers can use this architecture as a baseline for providing different extensions or implementing new approaches on top of CAMA that require context-aware monitoring. Practitioners may also use CAMA in their projects in order to manage contextual changes in an effective way.
Autores: Oscar Cabrera / Marc Oriol / Xavier Franch / Jordi Marco /
Palabras Clave: Context acquisition - Context life cycle - Context ontology - Context-aware computing - Software adaptation - Software reconfiguration
