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公开(公告)号：US12099791B1
公开(公告)日：2024-09-24
申请号：US17490496
申请日：2021-09-30
发明人： Shadi Saba , Roque Alejandro Arcudia Hernandez , Uyen Huynh Ha Nguyen , Pedro Eugênio Rocha Medeiros , Claire Liyan Ying , Ruozhi Zhang , Gustavo Emanuel Faria Araujo
IPC分类号： G06F30/333
CPC分类号： G06F30/333
摘要： An approach is disclosed herein for test sequence processing that is applicable to machine learning model generated test sequences as disclosed herein. The test sequence processing includes classification, grouping, and filtering. The classification is generated based on the execution of the test sequences. The grouping is performed based on information captured during the classification of the test sequences. The filtering is performed on a group by group basis to remove redundant test sequences.
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公开(公告)号：US12038477B1
公开(公告)日：2024-07-16
申请号：US17490426
申请日：2021-09-30
发明人： Shadi Saba , Roque Alejandro Arcudia Hernandez , Uyen Huynh Ha Nguyen , Pedro Eugênio Rocha Medeiros , Claire Liyan Ying
IPC分类号： G01R31/317 , G06N3/045 , G06N3/08
CPC分类号： G01R31/31704 , G01R31/31718 , G06N3/045 , G06N3/08
摘要： The approach disclosed herein is a new approach to sequence generation in the context of validation that relies on machine learning to explore and identify ways to achieve different states. In particular, the approach uses machine learning models to identify different states and ways to transition from one state to another. Actions are selected by machine learning models as they are being trained using reinforcement learning. This online inference also is likely to result in the discovery of not yet discovered states. Each state that has been identified is then used as a target to train a respective machine learning model. As part of this process a representation of all the states and actions or sequences of actions executed to reach those states is created. This representation, the respective machine learning models, or a combination thereof can then be used to generate different test sequences.
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