Mathematically Rigorous Methods for Determining Software Quality

Award Information
Agency:
Department of Defense
Branch
Navy
Amount:
$69,981.00
Award Year:
2010
Program:
STTR
Phase:
Phase I
Contract:
N00014-10-M-0250
Award Id:
95158
Agency Tracking Number:
N10A-035-0312
Solicitation Year:
n/a
Solicitation Topic Code:
NAVY 10T035
Solicitation Number:
n/a
Small Business Information
2021 Girard SE, Suite 150, Albuquerque, NM, 87106
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
794350025
Principal Investigator:
DeLesley Hutchins
Senior Software Engineer
(505) 245-9970
Delesley.Hutchins@mza.com
Business Contact:
Robert Praus
President
(505) 245-9970
Robert.Praus@mza.com
Research Institution:
University of California Santa Cruz
Cormac Flanagan
1156 High Street
Santa Cruz, CA, 95064
(831) 459-5375
Nonprofit college or university
Abstract
Current software development and testing methodologies are inadequate for validating software in mission-critical applications. As Dijstra famously stated: "testing can be used to show the presence of bugs, but never to show their absence." When lives and national security is at stake, there is a need for mathematically rigorous techniques that can verify the absence of bugs. We propose to develop a framework for software verification and validation using the technique of type-based static analysis. Type theory, and static analysis using types, has been extensively studied in the academic literature, but most of that research has not yet been used to build real-world tools for software verification. Type-based static analysis is both powerful and mathematically rigorous, and offers significant advantages in terms of scalability and modularity over competing techniques. Our proposed framework will be capable of analyzing code written in C, C++, Java, C#, and other languages, and will be able to analyze both source code and compiled binaries. The framework will use automatic type inference to derive appropriate safety properties for common programming patterns, but will also allow the user to supply annotations that guide the analysis algorithms when proving more difficult properties.

* information listed above is at the time of submission.

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