Automated analysis has been used in goal-oriented requirements engineering (GORE) to evaluate scenarios and make trade-off decisions. For higher complexity problems (e.g., backwards analysis), using a search-based solver may be more efficient than custom algorithms. When these black-box solvers produce a single solution, users may be suspicious about whether the given answer is ideal or believable. Users would like to explore the potential solutions but are prevented from doing so because these inquiries often suffer from a state explosion problem.

In this RE@Next! paper, we introduce the use of valuation-based filtering and coloring to assist users in understanding a solution space and selecting custom states from it. We use the concrete semantics of modeling requirements in the Evolving Intentions framework and its associated goal modeling tool, BloomingLeaf, to explore the application of these visualization techniques. In our initial evaluation, we demonstrate how these techniques can be used on a fully worked out example. We conduct initial measurements of the time savings and state space reduction created by the valuations and color filtering, and discuss future directions of this project.