This study investigates how interactive dashboards influence decision making by exploring how specific dashboard features impact design task performance, efficiency, understanding, and confidence. An experiment was conducted in which undergraduate student participants were given a design activity and randomly assigned to one of five dashboards, each using the same underlying functions but varying in the visualization features employed. These features include different graphical representations of the design decision inputs and performance outputs. Participants were first asked to use their assigned dashboard to design a catapult system that maximizes launch distance while meeting requirements related to height, weight, and cost. Following the design task, they were asked a series of questions about their experiences with the dashboard and their understanding of the catapult model. A between-subjects analysis then evaluated how the dashboard design influenced various outcomes of interest. The results show that students who used the most feature-rich dashboard did not perform objectively better than those with the most feature-sparse dashboard, though their self-reported performance was higher. The performance of female versus male participants was also compared, with no significant differences found. The findings support the notion that dashboards should be designed with minimal features to convey the necessary information, and they also point out the disconnect between objective performance and user-assessed performance with interactive dashboards.

The Simulink model tester "global assertions" capability developed by the software productivity consortium and T-VEC technologies is described in this paper. This assertions mechanism incorporates design assumptions or natural relationships with Simulink models to automatically generate more valid test vectors using the test automation framework. The positive impact this mechanism has on development and testing of Simulink models for the Lockheed Martin Space System's independent verification and validation team is also discussed

This paper describes the application of the Test Automation Framework on the Mars Polar Lander (MPL) software. The premature shutdown of the descent engine on the MPL spacecraft is believed to be the most likely cause for the mission failure. It is believed that the engine shutdown occurred when the three landing legs were extended into their deployed position. This event created an unanticipated transient touchdown indication from the legs, causing the software to inadvertently shutdown the descent engines prior to reaching the surface of Mars. This spurious indication should have been ignored by the Touchdown Monitor (TDM) software, but due to a design flaw, was actually stored in program variable thus causing the premature engine shutdown. The TAF approach was used to model the TDM software requirements. The associated TAF tools generated tests that identified a TDM fault that is the most likely cause of the mission failure.