Model transformation is an effective technique to produce target models from source models. Most transformation approaches focus on generating a single target model from a given source model. However, there are situations where a collection of possible target models is preferred over a single one. Such situations arise when some choices cannot be encoded in the transformation. Then, search techniques can be used to help find a target model having specific properties. In this paper, we present an approach that combines model transformation and constraint programming to generate explorable sets of models. We extend previous work by adding support for multiple solvers, as well as extending ATL, a declarative transformation language used to write such transformations. We evaluate our approach and language on a task scheduling case study including both scheduling constraints and schedule visualization.

模型转换是从源模型生成目标模型的有效技术。大多数转换方法着重于从给定的源模型生成单个目标模型。但是，在某些情况下，可能的目标模型集合比单个模型更受欢迎。当某些选择无法在转换中进行编码时，会出现这种情况。然后，可以使用搜索技术来帮助找到具有特定属性的目标模型。在本文中，我们提出了一种结合模型转换和约束编程以生成可探索模型集的方法。我们通过增加对多个求解器的支持以及扩展ATL（一种用于编写此类转换的声明性转换语言）来扩展以前的工作。