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Static Detection of Bugs in Embedded Software Using Lightweight Verification

Award Information

National Aeronautics and Space Administration
Award ID:
Program Year/Program:
2008 / SBIR
Agency Tracking Number:
Solicitation Year:
Solicitation Topic Code:
Solicitation Number:
Small Business Information
GrammaTech, Inc
531 Esty Street Ithaca, NY -
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Woman-Owned: No
Minority-Owned: No
HUBZone-Owned: No
Phase 2
Fiscal Year: 2008
Title: Static Detection of Bugs in Embedded Software Using Lightweight Verification
Agency: NASA
Contract: NNC08CA25C
Award Amount: $599,999.00


Validating software is a critical step in developing high confidence systems. Typical software development practices are not acceptable in systems where failure leads to loss of life or other high costs. Software best practices for high confidence systems are often codified as coding rules. Adhering to these practices can increase software readability and predictability, thereby enhancing quality. However, adherence is limited by the lack of high-quality tools to measure adherence automatically. Checking rule conformance requires a diverse set of software analysis technologies, ranging from syntactic analysis to sophisticated inference of runtime behavior. By combining lightweight verification techniques with other scalable analysis techniques that target syntactic and other static properties, we will create a tool that flags violations for almost all the rules typically applied to high-assurance code. Our Phase I work demonstrated the feasibility of this approach. In Phase I, we developed a tool for checking compliance with rules developed for JPL flight software. The tool leveraged GrammaTech's existing technology for static analysis, including facilities for analyzing a program's abstract syntax tree, control-flow graph, and inferred runtime behavior. The prototype successfully checks a set of rules designed for high-assurance software. Our experiments show that the tool adds only minimal overhead to our CodeSonar bug-finding tool, and generates few or no spurious results that could distract or annoy users.

Principal Investigator:

Michael McDougall
Principal Investigator

Business Contact:

Ray Teitelbaum
Small Business Information at Submission:

GrammaTech, Inc.
315-317 N. Aurora Street Ithaca, NY 14850

EIN/Tax ID: 161338879
Number of Employees:
Woman-Owned: No
Minority-Owned: No
HUBZone-Owned: No