Abstract-Millions of people use navigation software every day to commute and travel. In addition, many systems rely upon the correctness of navigation software to function, ranging from directions applications to self-driving machinery. Navigation software is difficult to test because it is hard or very expensive to evaluate its output. This difficulty is generally known as the oracle problem, a fundamental challenge in software testing. In this study, we propose a metamorphic testing strategy to alleviate the oracle problem in testing navigation software, and conduct a case study by testing the Google Maps mobile app, its web service API, and its graphical user interface. The results show that our strategy is effective with the detection of several real-life bugs in Google Maps. This study is the first work on automated testing of navigation software with the detection of real-life bugs.
The PICTURE-C mission will fly a 60 cm off-axis unobscured telescope and two high-contrast coronagraphs in successive high-altitude balloon flights with the goal of directly imaging and spectrally characterizing visible scattered light from exozodiacal dust in the interior 1-10 AU of nearby exoplanetary systems. The first flight in 2017 will use a 10´4 visible nulling coronagraph (previously flown on the PICTURE sounding rocket) and the second flight in 2019 will use a 10´7 vector vortex coronagraph. A low-order wavefront corrector (LOWC) will be used in both flights to remove time-varying aberrations from the coronagraph wavefront. The LOWC actuator is a 76-channel high-stroke deformable mirror packaged on top of a tip-tilt stage. This paper will detail the selection of a complementary high-speed, low-order wavefront sensor (LOWFS) for the mission. The relative performance and feasibility of several LOWFS designs will be compared including the Shack-Hartmann, Lyot LOWFS, and the curvature sensor. To test the different sensors, a model of the time-varying wavefront is constructed using measured pointing data and inertial dynamics models to simulate optical alignment perturbations and surface deformation in the balloon environment.
The Transiting Exoplanet Survey Satellite (TESS) is a NASA Explorer-class mission designed for finding exoplanets around nearby stars. TESS image data can also serve as a valuable resource for asteroid and comet detection, including near-Earth objects (NEOs). In order to exploit the TESS image data for moving object detection and potential object discovery, our team has developed an image processing pipeline as part of the Lincoln Near-Earth Asteroid Research (LINEAR) program, sponsored by the NASA NEO Observations Program. The LINEAR-TESS pipeline is currently in operation and reporting asteroid observations to the Minor Planet Center. In this paper we discuss the algorithms and methodology utilized to push the limits of the astrometric accuracy and photometric sensitivity of the TESS instrument for asteroid detection without a priori information on the ephemerides of the objects, and report on observation statistics from the first two years of TESS mission data.
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