Search NASA⌕ Search
Home/Archive/NASA NTRS

NASA NTRS · 20190002671

Command and Control System Software Development

Janmohamed, Nashir
View original source ↗Export metadata ↓

Abstract

With the first launch of the National Aeronautics and Space Administration's Space Launch System heavy-lift expendable launch vehicle and Lockheed Martin's Orion Multi-Purpose Crew Vehicle scheduled for the year 2020, there exists a need to complete development of a new command and control system that will provide systems monitoring and launch control for NASA's Exploration Missions. One remaining task necessary for completion of this command and control system is to create and maintain comprehensive unit tests of the control system software packages. These tests should verify that the implementation of all required and desired functionality works as intended. This testing infrastructure is mostly in place, but the control system's open source automation server still reports software "bugs" (possible flaws or failures which may lead to unintended behavior) and intermittently failing unit tests. Since code correctness is of critical importance for human rated software systems, I was assigned to diagnose the root cause of failing unit tests, eliminate non-determinism in these tests, and fix bugs as reported by the automation server.

Explore related subjects

GUIcontrol systemunit testingpython

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Janmohamed, Nashir. 2019-04-19. Command and Control System Software Development. https://ntrs.nasa.gov/citations/20190002671

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

NASA NTRS2017

Ask-the-Expert: Active Learning Based Knowledge Discovery Using the Expert

Often the manual review of large data sets, either for purposes of labeling unlabeled instances or for classifying meaningful results from uninteresting (but statistically significant) ones is extremely resource intensive, especially in terms of subject matter expert (SME) time. Use of active learning has been shown to diminish this review time significantly. However, since active learning is an iterative process of learning a classifier based on a small number of SME-provided labels at each iteration, the lack of an enabling tool can hinder the process of adoption of these technologies in real-life, in spite of their labor-saving potential. In this demo we present ASK-the-Expert, an interactive tool that allows SMEs to review instances from a data set and provide labels within a single framework. ASK-the-Expert is powered by an active learning algorithm for training a classifier in the back end. We demonstrate this system in the context of an aviation safety application, but the tool can be adopted to work as a simple review and labeling tool as well, without the use of active learning.

GUI↗