Search NASA⌕ Search
Home/Archive/NASA NTRS

NASA NTRS · 20210002579

Inserting software fault measurement techniques into development efforts

Nikora, A. P.
View original source ↗Export metadata ↓

Abstract

Over the past several years, techniques for estimating software fault content based on measurements of a system's structural evolution during its implementation have been developed.

Explore related subjects

softwarereliabilitymaintenancemeasurement

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Nikora, A. P.. 2000-07-24. Inserting software fault measurement techniques into development efforts. https://ntrs.nasa.gov/citations/20210002579

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

KEEP EXPLORING

Related reports

NASA NTRS2022

Advocating for Equality of Contribution: The Research Software Engineer (RSE)

Heliophysics depends on RSEs to properly engineer software. However, RSEs receive unequal treatment compared to their science counterparts, resulting in unsustainable talent loss. These restrictions include lack of credit for their contributions and insufficient training. This paper describes what a RSE is and proposes solutions, including implementing appropriate recognition standards.

software↗
NASA NTRS2020

Control System Software Development: Fall 2020 Internship Final Report

The Launch Control System (LCS) is an integral part of the Space Launch System (SLS) as it responds and sends instructions to both the vehicle and ground systems. This semester, the focus has been on the development of assurance tests for the Graphical User Interface (GUI) software components of the LCS. This will help ensure that the system software functions as intended. This paper describes the goals, procedures, and results of this process as well as lessons learned that may be useful for future interns working on similar projects.

software↗
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 backend. 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.

software↗