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Students’ experience with software testing in undergraduate computing courses is often relatively shallow, as compared to the importance of the topic. This experience report describes introducing industrial-strength testing into CMPSC 156, an upper division course in software engineering at UC Santa Barbara . We describe our efforts to modify our software engineering course to introduce rigorous test-coverage requirements into full-stack web development projects, requirements similar to those the authors had experienced in a professional software development setting. We present student feedback on the course and coverage metrics for the projects. We reflect on what about these changes worked (or didn’t), and provide suggestions for other instructors that would like to give their students a deeper experience with software testing in their software engineering courses. 

Current models of bending in soft arms are formulated in terms of experimentally determined, arm-specific parameters, which cannot evaluate fundamental differences in soft robot arm design. Existing models are successful at improving control of individual arms but do not give insight into how the structure of the arm affects the arm’s capabilities. For example, omnidirectional soft robot arms most frequently have three parallel actuators, but may have four or more, while common biological arms, including octopuses, have tens of distinct longitudinal muscle bundles. This article presents a quasi-static analytical model of soft arms bent with longitudinal actuators, based on equilibrium principles and assuming an unknown neutral axis location. The model is presented as a generalizable framework and specifically implemented for an arm with [Formula: see text] fluid-driven actuators, a subset of which are pressurized to induce a bend with a certain curvature and direction. The presented implementation is validated experimentally using planar (2D) and spatial (3D) bends. The planar model is used to initially estimate pressure for a closed-loop curvature control system and to bound the accessible configurations for a rapidly-exploring random trees (RRT) motion planner. A three-segment planar arm is demonstrated to navigate along a planned trajectory through a gap in a wall. Finally, the model is used to explore how the arm morphology affects maximum curvature and directional resolution. This research analytically connects soft arm structure and actuator behavior to unloaded arm performance, and the results may be used to methodically design soft robot arms.