2026-05-27T05:35:52Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/120902026-02-03T12:18:01Zcom_10630_2254col_10630_37959

Vallecillo-Moreno, Antonio Jesús Hilken, Frank Burgueño-Caballero, Lola Gogolla, Martin 2016-09-27T09:00:34Z 2016-09-27T09:00:34Z 2016-09-27 http://hdl.handle.net/10630/12090 Generating sample models for testing a model transformation is no easy task. This paper explores the use of classifying terms and stratified sampling for developing richer test cases for model transformations. Classifying terms are used to define the equivalence classes that characterize the relevant subgroups for the test cases. From each equivalence class of object models, several representative models are chosen depending on the required sample size. We compare our results with test suites developed using random sampling, and conclude that by using an ordered and stratified approach the coverage and effectiveness of the test suite can be significantly improved. eng open access by-nc-nd Soporte lógico Ingeniería del software Generating Effective Test Suites for Model Transformations Using Classifying Terms conference output