US 12,487,913 B2
Machine-learning based prediction of defect-prone components of information technology assets
Abhishek Mishra, Bangalore (IN); Vivek Bhargava, Bangalore (IN); Sharada Desai, Bangalore (IN); and Kumar Saurav, Bangalore (IN)
Assigned to Dell Products L.P., Round Rock, TX (US)
Filed by Dell Products L.P., Round Rock, TX (US)
Filed on Jun. 28, 2023, as Appl. No. 18/342,882.
Prior Publication US 2025/0004936 A1, Jan. 2, 2025
Int. Cl. G06F 11/3668 (2025.01); G06F 11/3604 (2025.01); G06F 11/362 (2025.01)
CPC G06F 11/3692 (2013.01) [G06F 11/3608 (2013.01); G06F 11/3648 (2013.01)] 20 Claims
OG exemplary drawing
 
1. An apparatus comprising:
at least one processing device comprising a processor coupled to a memory;
the at least one processing device being configured:
to determine one or more specifications for an information technology asset to be developed;
to identify, utilizing at least one machine learning model, whether at least one of the one or more specifications for the information technology asset is defect-prone, wherein a given one of the one or more specifications is identified as defect-prone responsive to at least one output of the at least one machine learning model indicating that the given specification has at least a threshold likelihood of resulting in one or more defects during development of the information technology asset;
to establish a mapping between the one or more identified defect-prone specifications for the information technology asset and one or more components of the information technology asset; and
to modify one or more development processes for the one or more components of the information technology asset that are mapped to the one or more identified defect-prone specifications;
wherein modifying the one or more development processes for the one or more components of the information technology asset comprises:
determining defect categories for the one or more identified defect-prone specifications for the information technology asset;
selecting, from a test script repository, one or more test scripts associated with one or more test cases utilized for the determined defect categories for one or more historical development processes to be utilized for testing the one or more components of the information technology asset mapped to the one or more identified defect-prone specifications;
applying the selected one or more test scripts as part of one or more testing processes during the one or more development processes for the information technology asset to identify one or more defects in the one or more components of the information technology asset that are mapped to the one or more identified defect-prone specifications; and
applying one or more fixes to correct the identified one or more defects in the one or more components of the information technology asset that are mapped to the one or more identified defect-prone specifications.