US 12,141,048 B2
Machine learning model for determining software defect criticality
Andriy Blazhiyevskiy, Santa Clara, CA (US); Eric Augustine Robison, Santa Clara, CA (US); Yijun Liu, San Diego, CA (US); Deependra Singh Jhala, Kirkland, WA (US); and Eugene Vikutan, Santa Clara, CA (US)
Assigned to ServiceNow, Inc., Santa Clara, CA (US)
Filed by ServiceNow, Inc., Santa Clara, CA (US)
Filed on Oct. 12, 2022, as Appl. No. 17/964,237.
Prior Publication US 2024/0126678 A1, Apr. 18, 2024
Int. Cl. G06F 11/36 (2006.01)
CPC G06F 11/3636 (2013.01) 20 Claims
OG exemplary drawing
 
1. A system comprising:
persistent storage containing a plurality of representations of a plurality of software defects identified in a software product; and
one or more processors configured to perform operations comprising:
determining, for each respective software defect of the plurality of software defects, corresponding one or more attribute values that represent a software development history associated with the respective software defect;
determining, for each respective software defect of the plurality of software defects, using a machine learning model, and based on the corresponding one or more attribute values, a corresponding escalation value representing a likelihood of the respective software defect being escalated for resolution after release of the software product, wherein the machine learning model has been trained using corresponding software development histories of a plurality of training software defects that were escalated for resolution after release of one or more prior versions of the software product;
based on the corresponding escalation value of each respective software defect, selecting, for resolution prior to the release of the software product, a defect subset comprising one or more software defects of the plurality of software defects;
storing a representation of the defect subset in the persistent storage; and
resolving, prior to release of the software product, at least one software defect of the one or more software defects of the defect subset.