Invention Grant
- Patent Title: Deep Q-network reinforcement learning for testing case selection and prioritization
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Application No.: US16998224Application Date: 2020-08-20
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Publication No.: US11249887B2Publication Date: 2022-02-15
- Inventor: Jianwu Xu , Haifeng Chen , Yuchen Bian
- Applicant: NEC Laboratories America, Inc.
- Applicant Address: US NJ Princeton
- Assignee: NEC Laboratories America, Inc.
- Current Assignee: NEC Laboratories America, Inc.
- Current Assignee Address: US NJ Princeton
- Agent Joseph Kolodka
- Main IPC: G06F9/44
- IPC: G06F9/44 ; G06F11/36 ; G06N20/00

Abstract:
Systems and methods for automated software test design and implementation. The system and method being able to establish an initial pool of test cases for testing computer code; apply the initial pool of test cases to the computer code in a testing environment to generate test results; preprocess the test results into a predetermined format; extract metadata from the test results; generate a training sequence; calculate a reward value for the pool of test cases; input the training sequence and reward value into a reinforcement learning agent; utilizing the value output from the reinforcement learning agent to produce a ranking list; prioritizing the initial pool of test cases and one or more new test cases based on the ranking list; and applying the prioritized initial pool of test cases and one or more new test cases to the computer code in a testing environment to generate test results.
Public/Granted literature
- US20210064515A1 DEEP Q-NETWORK REINFORCEMENT LEARNING FOR TESTING CASE SELECTION AND PRIORITIZATION Public/Granted day:2021-03-04
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