US 12,339,764 B2
Machine-learning based software testing technique
Nikolas Hermanns, Heinsberg (DE); Magued Sedra, Aachen (DE); Sujata Roy Chowdhury, Aachen (DE); Sarah Beschorner, Aachen (DE); and Alexandros Palaios, Moers (DE)
Assigned to Telefonaktiebolaget LM Ericsson (Publ), Stockholm (SE)
Appl. No. 18/007,246
Filed by Telefonaktiebolaget LM Ericsson (PUBL), Stockholm (SE)
PCT Filed Jul. 31, 2020, PCT No. PCT/EP2020/071705
§ 371(c)(1), (2) Date Jan. 27, 2023,
PCT Pub. No. WO2022/022838, PCT Pub. Date Feb. 3, 2022.
Prior Publication US 2023/0267073 A1, Aug. 24, 2023
Int. Cl. G06F 9/44 (2018.01); G06F 11/3668 (2025.01)
CPC G06F 11/3688 (2013.01) 17 Claims
OG exemplary drawing
 
1. A method of training a model for determining a test case (Ti) for software testing, wherein the software testing is based on a set of test cases built for a set of software modules (Mj) comprised by a software to be tested, the method comprising:
obtaining log information resulting from execution of each of the test cases, the log information being obtained per software module and per executed test case, the log information comprising one or more logged events;
performing supervised machine learning based on the log information obtained per software module and per executed test case as input to train a model for predicting the executed test case, the input to the model being representable as a set of training vectors, each training vector being associated with a particular executed test case and having a dimension corresponding to a number of software modules comprised by the set of software modules, each training vector having vector components derived from the log information; and
processing the log information prior to deriving the vector components, processing the log information obtained for a particular software module comprising pruning a portion of the log information pertaining to one or more logged events repeatedly occurring upon execution of the particular software module across the test cases.