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US Patent 11748232 System for discovering semantic relationships in computer programs

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Is a
Patent
Patent

Patent attributes

Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
Patent Number
11748232
Patent Inventor Names
Brian R. Muras
Michael L. Mattera
Dionny Santiago
John A. Maliani
Robert L. Vanderwall
David Adamo
Keith A. Briggs
Tariq King
Date of Patent
September 5, 2023
Patent Application Number
17468166
Date Filed
September 7, 2021
Patent Citations
‌
US Patent 7694181 Automated software testing framework
‌
US Patent 8473913 Method of and system for dynamic automated test case generation and execution
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US Patent 8479164 Automated test execution plan generation
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US Patent 8640084 Generating validation test suites
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US Patent 8683442 Software test case generation from a partial design model
‌
US Patent 8683446 Generation of test cases for functional testing of applications
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US Patent 9116725 Systems and methods for using virtualization of operating-system-level components to facilitate software testing
‌
US Patent 9223669 Self-expanding test automation method
...
Patent Primary Examiner
‌
Mongbao Nguyen
CPC Code
‌
G06F 11/3608
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G06F 16/288
‌
G06F 40/30

A system for discovering semantic relationships in computer programs is disclosed. In particular, the system may synergistically identify and validate semantic relationships, concepts, and groupings associated with data elements within a static or dynamic, time varying, source input. The system may utilize feature extractors to extract features from the input and reasoners to develop associations using data from multiple feature set types, and, can thus generate reliable, robust, and complete sets of semantic relationships from the input. The system may generate hypotheses associated with the relationships, concepts, and groupings, and validate the hypotheses by testing an application under evaluation by the system and observing the outputs generated from the testing. Information pertaining to validated or invalidated hypotheses may be provided to a learning engine to maximize reasoning and performance in subsequent discovery processes by adjusting models, vocabularies, dictionaries, parameters utilized by the system in identifying the relationships, concepts, and groupings.

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