Search NASA⌕ Search
Home/Archive/NASA NTRS

NASA NTRS · 20070018086

Experiences using Visualization Techniques to Present Requirements, Risks to Them, and Options for Risk Mitigation

Feather, Martin S.·Cornford, Steven L.·Kiper, James D.·Menzies, Tim
View original source ↗Export metadata ↓

Abstract

For several years we have been employing a risk-based decision process to guide development and application of advanced technologies, and for research and technology portfolio planning. The process is supported by custom software, in which visualization plays an important role. During requirements gathering, visualization is used to help scrutinize the status (completeness, extent) of the information. During decision making based on the gathered information, visualization is used to help decisionmakers understand the space of options and their consequences. In this paper we summarize the visualization capabilities that we have employed, indicating when and how they have proven useful.

Explore related subjects

requirementsvisualizationrisk

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Feather, Martin S., Cornford, Steven L., Kiper, James D., Menzies, Tim. 2006-09-09. Experiences using Visualization Techniques to Present Requirements, Risks to Them, and Options for Risk Mitigation. https://ntrs.nasa.gov/citations/20070018086

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

KEEP EXPLORING

Related reports

NASA NTRS2008

Optimizing Requirements Decisions with KEYS

Recent work with NASA's Jet Propulsion Laboratory has allowed for external access to five of JPL's real-world requirements models, anonymized to conceal proprietary information, but retaining their computational nature. Experimentation with these models, reported herein, demonstrates a dramatic speedup in the computations performed on them. These models have a well defined goal: select mitigations that retire risks which, in turn, increases the number of attainable requirements. Such a non-linear optimization is a well-studied problem. However identification of not only (a) the optimal solution(s) but also (b) the key factors leading to them is less well studied. Our technique, called KEYS, shows a rapid way of simultaneously identifying the solutions and their key factors. KEYS improves on prior work by several orders of magnitude. Prior experiments with simulated annealing or treatment learning took tens of minutes to hours to terminate. KEYS runs much faster than that; e.g for one model, KEYS ran 13,000 times faster than treatment learning (40 minutes versus 0.18 seconds). Processing these JPL models is a non-linear optimization problem: the fewest mitigations must be selected while achieving the most requirements. Non-linear optimization is a well studied problem. With this paper, we challenge other members of the PROMISE community to improve on our results with other techniques.

requirements↗