US 12,141,567 B2
Enhancing applications based on effectiveness scores
Saraswathi Sailaja Perumalla, Visakhapatnam (IN); Subha Kiran Patnaikuni, Visakhapatnam (IN); Venkata Vara Prasad Karri, Visakhapatnam (IN); and Sarbajit K. Rakshit, Kolkata (IN)
Assigned to INTERNATIONAL BUSINESS MACHINES CORPORATION, Armonk, NY (US)
Filed by INTERNATIONAL BUSINESS MACHINES CORPORATION, Armonk, NY (US)
Filed on Oct. 5, 2021, as Appl. No. 17/494,255.
Prior Publication US 2023/0105062 A1, Apr. 6, 2023
Int. Cl. G06F 8/65 (2018.01); G06F 11/34 (2006.01)
CPC G06F 8/65 (2013.01) [G06F 11/3409 (2013.01)] 18 Claims
OG exemplary drawing
 
1. A computer-implemented method, comprising operations for:
identifying application functionalities of applications in an application landscape;
accessing application logs and application landscape data for the applications, wherein the application landscape data comprises dependencies among the application functionalities and data sources used by the applications;
forming groups of the application functionalities, wherein each of the groups includes different application functionalities that perform a same function;
for a group of the groups, assigning an effectiveness score to each of the different application functionalities in the group of the groups, wherein the effectiveness score is calculated based on a time of execution that represents an execution duration for each of the different application functionalities to perform the same function and based on a type of data used by each of the different application functionalities;
identifying one or more application functionalities in the group of the groups that has a lower effectiveness score below a threshold;
generating a corresponding first recommendation that indicates another application functionality having a higher effectiveness score above the threshold for each of the one or more application functionalities having the lower effectiveness score by inputting the application logs and the application landscape data into a first machine learning model;
generating a second recommendation that indicates additional skills training for a team member based on the effectiveness score assigned to each of the different application functionalities that the team member worked on by inputting the application logs and the application landscape data into a second machine learning model;
automatically updating each of the one or more application functionalities having the lower effectiveness score based on the corresponding first recommendation; and
executing the applications with the updated one or more application functionalities in the application landscape.