US 12,013,770 B2
Smart selection of test scripts for commodity testing on manufacturing floor
Shibi Panikkar, Bandalore (IN); Shanir Anshul, Hyderabad (IN); Sudeshna Dash, Hyderabad (IN); Tuck Meng Chin, Singapore (SG); and Dong Ji, Shanghai (CN)
Assigned to Dell Products L.P., Round Rock, TX (US)
Appl. No. 17/426,975
Filed by Dell Products L.P., Round Rock, TX (US)
PCT Filed Feb. 1, 2019, PCT No. PCT/US2019/016334
§ 371(c)(1), (2) Date Jul. 29, 2021,
PCT Pub. No. WO2020/159539, PCT Pub. Date Aug. 6, 2020.
Prior Publication US 2022/0156168 A1, May 19, 2022
Int. Cl. G06F 11/00 (2006.01); G06F 11/263 (2006.01); G06F 11/273 (2006.01); G06F 11/277 (2006.01); G06N 20/00 (2019.01)
CPC G06F 11/263 (2013.01) [G06F 11/2733 (2013.01); G06F 11/277 (2013.01); G06N 20/00 (2019.01)] 13 Claims
OG exemplary drawing
 
1. A method characterized by comprising:
determining (202), by a computing system (104), that a first unit (106) is to be tested through execution of a set of unit-specific test scripts;
identifying (203), by the computing system, one or more test scripts in the set having at least one of the following two attributes:
the identified test script is more likely to fail during execution, and
the identified test script are time consuming to execute;
testing (204), by the computing system, the first unit by executing the identified one or more test scripts while skipping the execution of the remaining test scripts in the set;
executing, by the computing system, each test script in the set a pre-determined number of times for every second unit until a pre-defined number of second units are tested, wherein each second unit is identical to the first unit;
collecting, by the computing system, an execution-specific result for each test script in the set that is executed the pre-determined number of times for every second unit, wherein the execution-specific result for a corresponding test script in the set includes the following:
a first data linking result of execution of the corresponding test script with a vendor of a respective second unit for which the corresponding test script is executed,
a second data linking result of execution of the corresponding test script with a supplier of the respective second unit for which the corresponding test script is executed, and
a third data linking result of execution of the corresponding test script with the computing system where the respective second unit is tested;
training, by the computing system, a machine learning, ML, model using all execution-specific results for each test script in the set that is executed the pre-determined number of times for every second unit; and
using, by the computing system, the trained ML model to identify the one or more test scripts to be executed for testing the first unit and those whose execution is to be skipped.