AUTOMATED FEEDBACK-BASED APPLICATION OPTIMIZATION

Approaches presented herein enable optimization of a developing application to a user base. More specifically, application-centric data is gathered during a cultivation phase of the developing application. Substantially concurrently with the cultivation phase of the developing application, the application-centric data is analyzed according to static code of the developing application, a testing of the developing application, or a user experience (UX) design of the developing application. A machine learning model is applied to the analyzed application-centric data. This machine learning model is trained on historic application feedback data from applications available to the user base. Based on the machine learning model, a recommended change to optimize the developing application to the user base is generated.