Root cause analysis

A computer program product comprising:

• one or more computer readable hardware storage devices having computer readable program code stored therein, said program code containing instructions executable by a central processing unit (CPU) to implement a method for performing a root cause analysis, said method comprising:
• tracking, by the CPU, a focal point of a user's eye gaze;
• correlating, by the CPU, the focal point of the user's eye gaze to a viewing position of a display device displaying a file that includes event data being viewed by the user;
• identifying, by the CPU, as a function of the viewing position, events of interest in the event data and an amount of time that the event data is viewed by the user;
• identifying, by the CPU, an emotive expression of the user during an amount of time the user's eye glaze is focused on the viewing position,
• inserting, by the CPU, a numerical value assigned to the viewing position, the amount of time, the emotive expression and text of the event data, into a linear regression model;
• and
• outputting, by the CPU, as a function of the linear regression model, an interest score pertaining to one or more events of interest that were previously identified as a function of the user's eye gaze, said interest score being a probability of each identified event of interest being a root cause of a defect.

A computer system, comprising:

• a central processing unit (CPU);
• a memory device coupled to the CPU;
• and a computer readable storage device coupled to the processor, wherein the storage device contains program code executable by the CPU via the memory device to implement a method for performing a root cause analysis, said method comprising:
• tracking, by the CPU, a focal point of a user's eye gaze;
• correlating, by the CPU, the focal point of the user's eye gaze to a viewing position of a display device displaying a file that includes event data being viewed by the user;
• identifying, by the CPU, as a function of the viewing position, events of interest in the event data and an amount of time that the event data is viewed by the user;
• identifying, by the CPU, an emotive expression of the user during an amount of time the user's eye glaze is focused on the viewing position,
• inserting, by the CPU, a numerical value assigned to the viewing position, the amount of time, the emotive expression and text of the event data, into a linear regression model;
• and
• outputting, by the CPU, as a function of the linear regression model, an interest score pertaining to one or more events of interest that were previously identified as a function of the user's eye gaze, said interest score being a probability of each identified event of interest being a root cause of a defect.