Iterative Generation of Diverse Models for Testing Specifications of DSL Tools
Oszkár Semeráth, Dániel Varró · Lecture notes in computer science · 2018
The validation of modeling tools of custom domain-specific languages (DSLs) frequently relies upon an automatically generated set of models as a test suite. While many software testing approaches recommend that this test suite should be diverse, model diversity has not been studied systematically for graph models. In the paper, we propose diversity metrics for models by exploiting neighborhood shapes as abstraction. Furthermore, we propose an iterative model generation technique to synthesize a diverse set of models where each model is taken from a different equivalence class as defined by neighborhood shapes. We evaluate our diversity metrics in the context of mutation testing for an industrial DSL and compare our model generation technique with the popular model generator Alloy. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.