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Self-Coding Cyber Fixes

Award Information
Agency: Department of Defense
Branch: Missile Defense Agency
Contract: HQ0860-20-C-7017
Agency Tracking Number: B192-009-0141
Amount: $150,000.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: MDA19-009
Solicitation Number: 19.2
Solicitation Year: 2019
Award Year: 2020
Award Start Date (Proposal Award Date): 2019-11-22
Award End Date (Contract End Date): 2021-03-21
Small Business Information
591 Camino de la Reina Suite 610
San Diego, CA 92108
United States
DUNS: 010681380
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 John Geddes
 Senior Staff Scientist
 (619) 398-1410
Business Contact
 Robert McGraw
Phone: (619) 398-1410
Research Institution

RAM Laboratories is proposing Deep Learning for Precise, Automatic and Trusted Code Hardening and Error Removal (DL-PATCHER) which is a combination of a large and diverse source code dataset built from publicly available repositories to be used for training state-of-the-art deep learning algorithms able to produce a model that can automatically generate patches that fix bugs reported by source code analysis tools. In addition, the patch verifier is built utilizing a wide set of heuristics that are able to not only confirm the accuracy of the patch, but also ensures that no major and unexpected feature modifications are made which could significantly impact core functionality. Approved for Public Release | 19-MDA-10270 (18 Nov 19)

* Information listed above is at the time of submission. *

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