Qualifying input test data for model transformations

Multiple Fuzzy Regression Model for Fuzzy Input-Output Data

A novel approach to the problem of regression modeling for fuzzy input-output data is introduced.In order to estimate the parameters of the model, a distance on the space of interval-valued quantities is employed.By minimizing the sum of squared errors, a class of regression models is derived based on the interval-valued data obtained from the $alpha$-level sets of fuzzy input-output data.Then,...

Traceability as Input for Model Transformations

Some model transformations require more information than can be derived from its source model(s) in order to generate a meaningful target model. For example, a transformation with two source models needs to know how their respective model elements relate; these relations often only exist implicitly as part of the transformations developer’s knowledge. In this paper we show that traceability mod...

Testing Model Transformations: A case for Test Generation from Input Domain Models

Model transformations can automate critical tasks in model-driven development. Thorough validation techniques are required to ensure their correctness. In this lecture we focus on testing model transformations. In particular, we present an approach for systematic selection of input test data. This approach is based on a key characteristic of model transformations: their input domain is formally...

Specification-Driven Test Generation for Model Transformations

A Randomness Test for Stable Data

In this paper, we propose a new method for checking randomness of non-Gaussian stable data based on a characterization result. This method is more sensitive with respect to non-random data compared to the well-known non-parametric randomness tests.