Recent approaches have investigated assisting users in making early trade-off decisions when the future evolution of project elements is uncertain. These approaches have demonstrated promise in their analytical capabilities; yet, stakeholders have expressed concerns about the readability of the models and resulting analysis, which builds upon Tropos. Tropos is based on formal semantics enabling automated analysis; however, this creates a problem of interpreting evidence pairs. The aim of our broader research project is to improve the process of model comprehension and decision making by improving how analysts interpret and make decisions. We extend and evaluate a prior approach, called EVO, which uses color to visualize evidence pairs. In this scientific evaluation paper, we explore the effectiveness and usability of EVO. We conduct an experiment (n=32) to measure any effect of using colors to represent evidence pairs. We find that with minimal training, untrained modelers were able to use the color visualization for decision making. The visualization significantly improves the speed of model comprehension and users found it helpful.
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A Divide & Concur Approach to Collaborative Goal Modeling with Merge in Early-RE
Goal modeling enables the elicitation of stakeholders’ intentionality in the earlier stages of a project. Often, approaches are limited by the effort required to create an initial goal model. In this paper, we investigate the problem of model merging for Tropos goal models. Specifically, we propose a formal approach to the problem of automatically merging the attributes of intentions and actors, once these elements have been matched. Additionally, recent approaches have investigated answering questions about future evolutions of stakeholders’ projects with goal models. In this work we consider both static models, as well as those with timing information, using the principles of gullibility, contradiction, and consensus. We study our implementation and validate the merge operation on a variety of models from the literature.
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- Award ID(s):
- 2104732
- NSF-PAR ID:
- 10402610
- Date Published:
- Journal Name:
- 2022 IEEE 30th International Requirements Engineering Conference (RE)
- Page Range / eLocation ID:
- 14 to 25
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/RE54965.2022.00009
