Publication

TINA Service Validation: The ErnesTINA Project

Jean-Pierre Hubaux, Xavier Logean
1997
Reports

Abstract

While extensive work has been carried out with the goal of validating the TINA architecture and the TINA documents, little has been done yet for the validation of TINA services. This is the main focus of the ErnesTINA project. In the ErnesTINA project, we propose an integrated approach to facilitate the validation of TINA services by verifying at run-time that the service implementation has not violated and is not violating certain predefined properties. In this paper, we present the specification of the properties, the run-time observation of the distributed environment, the validation of the properties and finally the implementation of the concepts in a prototype.

Official source

https://infoscience.epfl.ch/entities/publication/f8a1b3b9-d60d-4f41-be45-ab42047a33b6

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Jean-Pierre Hubaux, Xavier Logean
1997
Reports

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/f8a1b3b9-d60d-4f41-be45-ab42047a33b6

About this result

Related concepts (32)

Verification and validation

Verification and validation (also abbreviated as V&V) are independent procedures that are used together for checking that a product, service, or system meets requirements and specifications and that it fulfills its intended purpose. These are critical components of a quality management system such as ISO 9000. The words "verification" and "validation" are sometimes preceded with "independent", indicating that the verification and validation is to be performed by a disinterested third party.

Statistical model validation

In statistics, model validation is the task of evaluating whether a chosen statistical model is appropriate or not. Oftentimes in statistical inference, inferences from models that appear to fit their data may be flukes, resulting in a misunderstanding by researchers of the actual relevance of their model. To combat this, model validation is used to test whether a statistical model can hold up to permutations in the data.

Cross-validation (statistics)

Cross-validation, sometimes called rotation estimation or out-of-sample testing, is any of various similar model validation techniques for assessing how the results of a statistical analysis will generalize to an independent data set. Cross-validation is a resampling method that uses different portions of the data to test and train a model on different iterations. It is mainly used in settings where the goal is prediction, and one wants to estimate how accurately a predictive model will perform in practice.

Related publications (2)

TINA Service Validation: The ErnesTINA Project

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TINA Service Validation: The ErnesTINA project