Multi-data analysis based proactive defect detection and resolution

A method for multi-data analysis based proactive defect detection and resolution, the method comprising:

• performing, by a data analyzer that is executed by at least one hardware processor,
• a sentiment analysis on consumer data related to an application to determine a sentiment of the consumer data related to the application, and
• a language processing analysis, in response to a determination that the sentiment is a negative sentiment, on the consumer data related to the application to determine a function associated with the negative sentiment;
• analyzing, by a defect detector that is executed by the at least one hardware processor, in response to the determination that the sentiment is the negative sentiment, application code and process data related to the application to determine a defect associated with the application by
• comparing a new user interaction pattern with the application to a previous user interaction pattern with the application, and
• in response to a determination that the new user interaction pattern with the application is different from the previous user interaction pattern with the application, identifying the defect associated with the application based on the difference of new user interaction pattern with the application from the previous user interaction pattern with the application;
• and
• modifying, by a defect resolver that is executed by the at least one hardware processor,
• a code of the application to correct the defect associated with the application.

A non-transitory computer readable medium having stored thereon machine readable instructions for multi-data analysis based proactive defect detection and resolution, the machine readable instructions when executed cause at least one hardware processor to:

• perform, by a data analyzer that is executed by at least one hardware processor,
• a sentiment analysis on consumer data related to an application to determine a sentiment of the consumer data related to the application, and
• a language processing analysis on the consumer data related to the application to determine a function associated with the sentiment;
• analyze, by a defect detector that is executed by the at least one hardware processor, application code and process data related to the application to determine a defect associated with the application by
• comparing a new user interaction pattern with the application to a previous user interaction pattern with the application to identify the defect associated with the application;
• determine, by a defect ticket generator that is executed by the at least one hardware processor, a context of the defect associated with the application;
• and
• generate, by the defect ticket generator, a defect ticket that includes an indication of the context of the defect associated with the application.