Granted Patent B2

US 12,561,231 · App. 18/141,198 · Granted Feb 24, 2026

Inventors: Basavaraj Veerappa Somawagol (Bangalore, IN); Balaji Thangavelu (Bangalore, IN); Sreekanth Sreedevi Sasidharan (Bangalore, IN)

Assignee: Infosys Limited

G06F11/3676G06F11/3684

Abstract

Claims (37)

1 . A method for determination of maturity of test automation, the method comprising:

receiving, by a computing device, input data comprising user responses to a questionnaire associated with a test case, wherein the questionnaire comprises a first set of questions associated with a coverage of test case automation, and a second set of questions associated with a usefulness of test case automation;

determining, by the computing device, a first set of weightages corresponding to the first set of questions and a second set of weightages corresponding to the second set of questions using an Artificial Intelligence (AI) model, wherein the determining comprises of:

dynamically updating the first set of weightages and the second set of weightages based on historical data of the test case, wherein the historical data comprises data corresponding to the first set of questions, the second set of questions, and previous weightages associated with each of the first set of questions and the second set of questions;

computing, by the computing device, a coverage of automation index based on the user responses and the first set of weightages corresponding to the first set of questions, and a usefulness of automation index based on the user responses and the second set of weightages corresponding to the second set of questions;

generating, by the computing device, a recommendation corresponding to automation of the test case based on the coverage of automation index and the usefulness of automation index;

transforming each of the user responses into a numerical value within a common scale based on a set of predefined rules;

generating a chart representing the test case on a first Graphical User Interface (GUI) based on the coverage of automation index and the usefulness of automation index, wherein:

the chart comprises an x-axis representing values of the coverage of automation index and a y-axis representing values of the usefulness of automation index,

the chart comprises four quadrants formed by extending a perpendicular line from a mid-point of the common scale of each of the x-axis and the y-axis, and

the test case is represented in a quadrant of the four quadrants; and

assigning a category from a set of categories to the test case based on the quadrant associated with the test case, wherein each of the set of categories corresponds to an automation feasibility label.

2 . The method of claim 1 , wherein the input data further comprises the historical data of the test case.

3 . The method of claim 2 , further comprising generating, by the computing device, one or more questions of the questionnaire through a generative Artificial Intelligence (AI) model based on the test case and the historical data of the test case.

4 . The method of claim 1 , further comprising assigning, by the computing device, a category from a set of categories to the test case based on the quadrant associated with the test case, wherein each of the set of categories further corresponds to an automation maturity label.

5 . The method of claim 1 , further comprising rendering, by the computing device and through a second GUI, the generated recommendation.

6 . The method of claim 1 , further comprising determining, by the computing device, an automation maturity percentage of the test case based on the coverage of automation index and the usefulness of automation index.

7 . The method of claim 1 , further comprising rendering, by the computing device and through a third GUI, a review of current automation of the test case based on the coverage of automation index and the usefulness of automation index.

8 . A system for determination of maturity of test automation, the system comprising:

a processing circuitry; and

a memory communicatively coupled to the processing circuitry, wherein the memory stores processor instructions, which when executed by the processing circuitry, cause the processing circuitry to:

receive input data comprising user responses to a questionnaire associated with a test case, wherein the questionnaire comprises a first set of questions associated with a coverage of test case automation, and a second set of questions associated with a usefulness of test case automation;

determine a first set of weightages corresponding to the first set of questions and a second set of weightages corresponding to the second set of questions using an Artificial Intelligence (AI) model, and wherein to determine the first set of weightages and the second set of weightages, the processing circuitry is further caused to:

dynamically update the first set of weightages and the second set of weightages based on historical data of the test case, wherein the historical data comprises data corresponding to the first set of questions, the second set of questions, and previous weightages associated with each of the first set of questions and the second set of questions;

compute a coverage of automation index based on the user responses and the first set of weightages corresponding to the first set of questions, and a usefulness of automation index based on the user responses and the second set of weightages corresponding to the second set of questions;

generate a recommendation corresponding to automation of the test case based on the coverage of automation index and the usefulness of automation index;

transform each of the user responses into a numerical value within a common scale based on a set of predefined rules;

generate a chart representing the test case on a first Graphical User Interface (GUI) based on the coverage of automation index and the usefulness of automation index, wherein:

the chart comprises an x-axis representing values of the coverage of automation index and a y-axis representing values of the usefulness of automation index,

the chart comprises four quadrants formed by extending a perpendicular line from a mid-point of the common scale of each of the x-axis and the y-axis, and

the test case is represented in a quadrant of the four quadrants; and

assign a category from a set of categories to the test case based on the quadrant associated with the test case, wherein each of the set of categories corresponds to an automation feasibility label.

9 . The system of claim 8 , wherein the input data further comprises the historical data of the test case.

10 . The system of claim 9 , wherein the processor instructions, on execution, further cause the processing circuitry to generate one or more questions of the questionnaire through a generative Artificial Intelligence (AI) model based on the test case and the historical data of the test case.

11 . The system of claim 8 , wherein the processor instructions, on execution, further cause the processing circuitry to render, through a second GUI, the generated recommendation.

12 . The system of claim 8 , wherein the processor instructions, on execution, further cause the processing circuitry to determine a target automation percentage of the test case based on the coverage of automation index and the usefulness of automation index.

13 . The system of claim 8 , wherein the processor instructions, on execution, further cause the processing circuitry to render, through a third GUI, a review of current automation of the test case based on the coverage of automation index and the usefulness of automation index.

Assignments (1)

Priority Claims (2)

Continuity (2)

References Cited (13)