Abstract: Certain aspects of the present disclosure provide techniques for handling crash events in a software application using application-agnostic machine learning models. An example method generally includes receiving a data set of crash reports from a software application for analysis. Using a first neural network, a representation of each respective crash report in the data set is generated. The data set of crash reports and a mapping between functions in the software application and a multidimensional space are input into the first neural network. Each respective crash report in the data set is classified using a second neural network and the representation of each crash report in the data set. One or more actions are taken with respect to the software application based on the classifying each respective crash report in the data set.

Abstract: Certain aspects of the present disclosure provide techniques for handling crash events in a software application using application-agnostic machine learning models. An example method generally includes receiving a data set of crash reports from a software application for analysis. Using a first neural network, a representation of each respective crash report in the data set is generated. The data set of crash reports and a mapping between functions in the software application and a multidimensional space are input into the first neural network. Each respective crash report in the data set is classified using a second neural network and the representation of each crash report in the data set. One or more actions are taken with respect to the software application based on the classifying each respective crash report in the data set.

Abstract: Certain aspects of the present disclosure provide techniques for determining crash similarity based on stack traces and user actions associated with a crash event and handling crash events based on the determined crash similarity. An example method generally includes receiving a crash report for a crash event recorded during execution of a software application. A similarity score is generated between the received crash report and one or more historical crash reports associated with historical crash events recorded during execution of the software application. A target set of historical crash reports is selected based on the generated similarity scores for each respective historical crash report. The target set of historical crash reports generally includes crash reports having generated similarity scores exceeding a threshold similarity score. One or more actions are taken to process the received crash report based on the target set of historical crash reports.

Abstract: A crash analysis system for identifying the cause of application crashes. The crash analysis system generates mapped stack traces that combine code version history data with logging information. The mapped stack traces are used to identify the causal stack frame which includes the function that caused the application crash. The crash analysis system also identifies the author of the function that caused the application crash and distributes a notification to the author that prompts the author to develop a fix that resolves the application crash.