Abstract: Systems and methods may be implemented to prevent application crashes by correlating a history of operating system (OS) updates with occurrence of past client application crashes using information that is crowd-sourced from multiple information handling systems so that action/s may be taken to prevent occurrence of future client application crashes on the information handling system/s. Machine learning (e.g., deep learning) may be employed to automatically correlate the history of OS updates with a record of past client application crashes that have occurred on multiple client information handling systems, and then the likely root cause/s for the client application crashes identified based on this correlation. These likely root cause/s may be corrected or otherwise addressed, e.g., by further investigation into the details of the root cause, and/or user or automatic system action to remove or block the root cause.

Abstract: Systems and methods may be implemented to prevent application crashes by correlating a history of operating system (OS) updates with occurrence of past client application crashes using information that is crowd-sourced from multiple information handling systems so that action/s may be taken to prevent occurrence of future client application crashes on the information handling system/s. Machine learning (e.g., deep learning) may be employed to automatically correlate the history of OS updates with a record of past client application crashes that have occurred on multiple client information handling systems, and then the likely root cause/s for the client application crashes identified based on this correlation. These likely root cause/s may be corrected or otherwise addressed, e.g., by further investigation into the details of the root cause, and/or user or automatic system action to remove or block the root cause.