The Reliability of Embedded Software-Based Systems: Novel Ways to Enhance and Predict

Award Information
Agency:
Department of Defense
Branch
Office of the Secretary of Defense
Amount:
$99,993.00
Award Year:
2007
Program:
STTR
Phase:
Phase I
Contract:
W911QX-07-C-0076
Agency Tracking Number:
O064-SP4-2034
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
MIGMA SYSTEMS, INC.
1600 Providence Highway, Walpole, MA, 02081
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
125933916
Principal Investigator:
Michael Zeifman
Senior Research Engineer
(508) 660-0328
mzeifman@migmasys.com
Business Contact:
Bo Ling
President & CEO
(508) 660-0328
bling@migmasys.com
Research Institution:
UNIV. OF PENNSYLVANIA
Insup Lee
Department of Computer and Inf, 3330 Walnut Street
Philadephia, PA, 19104 6389
(215) 898-3532
Nonprofit college or university
Abstract
Embedded systems have become increasingly popular over the past years, both in civil and military applications. The probability of an embedded system to perform the required operations during the specified period of time, i.e., the reliability, depends on both the underlying hardware reliability and software reliability. The traditional approach to predict the software reliability on the basis of the programmatic errors implies specially designed testing and predictive models. The main problem of this approach is that it usually considers a parametric model with the parameters to be estimated during the testing, whereas there is no physical or mathematical law underlying the parametric model itself. The proposed method originates from a Bayesian approach so that its dependence on the initial underlying parametric model is relatively weak. At the same time, it accounts for the previous/expert data in a most effective way. To enhance the reliability essential in safety-critical embedded systems, we will extend our method of conversion the informal design requirements into the reference specifications via the EFSM (Extended Finite State Machines).

* information listed above is at the time of submission.

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