Crash bug component prediction system to identify components as potential bug sources

1. A computer-implemented method for identifying components in crash reports generated in response to crashes of software systems, the method being executed by one or more processors and comprising:

receiving a crash report provided as a computer-readable file;

providing a stack trace from the crash report;

adding component information to the stack trace to provide a stack trace and component table comprising a component column populated with a set of the components, a component position column populated with a set of component positions, each component in the set of components being associated with a respective component position in the set of component positions, a function name column populated with a set of function names, each component in the set of the components being associated with a sub-set of function names, and a function value column populated with a set of function values, each component being associated with a sub-set of function values;

for each component identified in the stack trace, determining a set of features;

processing sets of features through a machine learning (ML) model to provide a prediction identifying a component of the set of the components as a bug component, the ML model having been trained using training data comprising, for each stack trace in a set of historical stack traces, a set of historical features and a label, the set of historical features corresponding to a bug component for the respective stack trace; and

assigning the bug component for resolution through a crash management system.

2. The method of claim 1 , wherein the prediction comprises a component position within the stack trace.

3. The method of claim 1 , wherein the set of features of a respective component comprises at least one feature determined based on the sub-set of function values of the respective component.

4. The method of claim 1 , wherein each function value in the set of function values comprises an inverse document frequency (IDF) value determined for a respective function, the IDF value indicating a relative commonality of the respective function within a set of crash reports.

5. The method of claim 1 , wherein the component information comprises, for each component, component name and location, and the component information is added using a component map stored in computer-readable memory.

6. The method of claim 1 , wherein providing the stack trace from the crash report comprises generating a stack trace table comprising functional serial numbers and function names listed in the stack trace of the crash report.

7. A non-transitory computer-readable storage medium coupled to one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations for identifying components in crash reports generated in response to crashes of software systems, the operations comprising:

receiving a crash report provided as a computer-readable file;

providing a stack trace from the crash report;

adding component information to the stack trace to provide a stack trace and component table comprising a component column populated with a set of the components, a component position column populated with a set of component positions, each component in the set of components being associated with a respective component position in the set of component positions, a function name column populated with a set of function names, each component in the set of the components being associated with a sub-set of function names, and a function value column populated with a set of function values, each component being associated with a sub-set of function values;

for each component identified in the stack trace, determining a set of features;

processing sets of features through a machine learning (ML) model to provide a prediction identifying a component of the set of the components as a bug component, the ML model having been trained using training data comprising, for each stack trace in a set of historical stack traces, a set of historical features and a label, the set of historical features corresponding to a bug component for the respective stack trace; and

assigning the bug component for resolution through a crash management system.

8. The non-transitory computer-readable storage medium of claim 7 , wherein the prediction comprises a component position within the stack trace.

9. The non-transitory computer-readable storage medium of claim 7 , wherein the set of features of a respective component comprises at least one feature determined based on the sub-set of function values of the respective component.

10. The non-transitory computer-readable storage medium of claim 9 , wherein each function value in the set of function values comprises an inverse document frequency (IDF) value determined for a respective function, the IDF value indicating a relative commonality of the respective function within a set of crash reports.

11. The non-transitory computer-readable storage medium of claim 7 , wherein the component information comprises, for each component, component name and location, and the component information is added using a component map stored in computer-readable memory.

12. The non-transitory computer-readable storage medium of claim 7 , wherein providing the stack trace from the crash report comprises generating a stack trace table comprising functional serial numbers and function names listed in the stack trace of the crash report.

13. A system, comprising:

a computing device; and

a computer-readable storage device coupled to the computing device and having instructions stored thereon which, when executed by the computing device, cause the computing device to perform operations for identifying components in crash reports generated in response to crashes of software systems, the operations comprising:

receiving a crash report provided as a computer-readable file;

providing a stack trace from the crash report;

adding component information to the stack trace to provide a stack trace and component table comprising a component column populated with a set of the components, a component position column populated with a set of component positions, each component in the set of components being associated with a respective component position in the set of component positions, a function name column populated with a set of function names, each component in the set of the components being associated with a sub-set of function names, and a function value column populated with a set of function values, each component being associated with a sub-set of function values;

for each component identified in the stack trace, determining a set of features;

processing sets of features through a machine learning (ML) model to provide a prediction identifying a component of the set of the components as a bug component, the ML model having been trained using training data comprising, for each stack trace in a set of historical stack traces, a set of historical features and a label, the set of historical features corresponding to a bug component for the respective stack trace; and

assigning the bug component for resolution through a crash management system.

14. The system of claim 13 , wherein the prediction comprises a component position within the stack trace.

15. The system of claim 13 , wherein the set of features of a respective component comprises at least one feature determined based on the sub-set of function values of the respective component.

16. The system of claim 15 , wherein each function value in the set of function values comprises an inverse document frequency (IDF) value determined for a respective function, the IDF value indicating a relative commonality of the respective function within a set of crash reports.

17. The system of claim 13 , wherein the component information comprises, for each component, component name and location, and the component information is added using a component map stored in computer-readable memory.