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Browning, Tyson R.; Fricke, Ernst; Negele, Herbert

doi:10.1002/sys.v9:2

Cited by 39 publications

(1 citation statement)

References 97 publications

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“…Also in other contexts, several techniques and tools have been proposed for requirements visualization. PaladinRM (Austin et al 2006 These visualization mechanisms were tested with some case studies that suggest that further visualization mechanisms could support analysis. In (Ernst et al 2006), the authors present graphical and textual annotations in i* diagrams to denote four quality attributes (NFRs): degree of certainty, feasibility, trustability and performance of a goal.…”

Section: Related Workmentioning

confidence: 99%

Evaluating different i*-based approaches for selecting functional requirements while balancing and optimizing non-functional requirements: A controlled experiment

Zubcoff¹,

Garrigós²,

Casteleyn³

et al. 2019

Information and Software Technology

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 When modelling, subjects may perform better using alternative i*-derived models and visualizations for particular tasks.  As models become complex, Pareto front based visualization outperforms original i* notation when selecting functional requirement to implement while balancing nonfunctional requirements.  It is 3.51 times more likely to solve modelling tasks correctly using Pareto front tabular or radar chart visualization.  Subjects solve complex modelling tasks quicker using Pareto front tabular (around 10% faster) or radar chart visualization (around 30% faster).  Pareto front tabular produces more correct answer; radar chart visualization is faster.  Experience helps in solving the exercises quicker, but has no effect on amount of correct answers.  Visual or textual learning preference has no effect on score.

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Section: Related Workmentioning

confidence: 99%