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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 article, we explore the effectiveness of EVO with and without the impacts of tooling through a two-phased empirical study. All subjects in both phases were untrained modelers, given training at study time. First, we conduct an experiment to measure any effect of using colors to represent evidence pairs. Second, we explore how subjects engage in decision-making activities (with or without color) through a user study. We find that the EVO color visualization significantly improves the speed of model comprehension and is perceived as helpful by study subjects. 

Recent approaches have investigated assisting users in making early trade-off decisions when the future evolution of project elements is uncertain. These approaches have demon-strated 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. 

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.