Toward practical adoption of i* framework: an automatic two-level layout approach


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.

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  1. 1.

    The prototype tool (Two-level iStar Layout) can be accessed at

  2. 2.

    All the instruments used in this controlled experiment can be found here:, which includes the data set where our iStar models come from, auto-layout and manual-layout models used in the controlled experiment, questionnaires and the iStar 2.0 tutorial slides.

  3. 3.

    Cases and the questionnaire can be found here:

  4. 4.

    Visit this link to experience the process of laying out the largest model in the performance testing:


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Correspondence to Tong Li.

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Wang, Y., Li, T., Zhou, Q. et al. Toward practical adoption of i* framework: an automatic two-level layout approach. Requirements Eng (2021).

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  • i* framework
  • Automatic layout
  • Empirical study
  • Prototype tool