Bridging the gap between academia and industry is an important issue to promote the practicality of i* framework. Researchers have been dealing with this issue from various perspectives, such as simplifying the meta-models or modeling processes of i* framework. In this paper, we exclusively focus on the scalability issue in laying out large-scale i* models and propose a two-level layout approach to automatically lay out i* models in an efficient and comprehensible manner, contributing to the adoption of i* framework in the industry. The proposed approach is designed by considering the semantics of i* constructs and layout conventions of i* models in order to produce meaningful layouts and can appropriately handle both the SD (Strategic Dependency) view and the SR (Strategic Rationale) view of i* models. We have implemented our approach in an open-access prototype tool, which is able to be integrated with existing iStarML-compatible modeling tools. We have conducted a controlled experiment, a case study, and performance testing to empirically and comprehensively evaluate the utility of our approach, the results of which show that our proposal can efficiently produce meaningful layouts that are as comprehensible as manually laid out models in most cases.

缩小学术界和行业之间的差距是促进i *框架实用性的重要问题。研究人员已从各种角度处理此问题，例如简化了i *框架的元模型或建模过程。在本文中，我们仅关注在布置大型i *模型时的可伸缩性问题，并提出了一种两级布局方法来以高效且易于理解的方式自动布置i *模型，从而有助于采用i *框架在行业中。通过考虑i *构造的语义和i *模型的布局约定来设计所提出的方法，以便产生有意义的布局，并且可以适当地处理i *模型的SD（策略相关性）视图和SR（策略理性）视图。我们已经在开放式原型工具中实现了我们的方法，该工具可以与现有的iStarML兼容建模工具集成。我们进行了一项对照实验，一个案例研究和性能测试，以经验地和全面地评估该方法的实用性，其结果表明，我们的建议可以有效地产生有意义的布局，这些布局在大多数情况下都可以像手动布置的模型一样容易理解。