AGILE ESTIMATION

• US 20160077832A1
• Filed: 09/17/2014
• Published: 03/17/2016
• Est. Priority Date: 09/17/2014
• Status: Active Grant

• Current Assignee: TATA Consultancy Services Limited (Tata Sons Pvt Ltd.)
• Original Assignee: TATA Consultancy Services Limited (Tata Sons Pvt Ltd.)
• Inventors: BHATTACHARYYA, Pranabendu, DAS, Sharmila, Roy, Sayantan
• Granted Patent: US 10,642,611 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 8/77 Software metrics
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/06311 Scheduling, planning or task assignment for a person or group

Abstract

The present subject matter relates to size estimation of a plurality of user stories for agile software development. The plurality of user stories associated with a software may be obtained, and a base size for each of the plurality of user stories may be computed based on at least one basic parameter. The at least one basic parameter is indicative of a parameter affecting complexity associated with execution of a user story. Subsequently, an additional size for the plurality of user stories may be ascertained, based on at least one additional parameter. The at least one additional parameter is indicative of an environmental factor affecting delivery of the software. The overall size of the plurality of user stories is determined by consolidating the base size for each user story with the additional size for the plurality of user stories.

Claims

1. A method for size estimation of a plurality of user stories for agile software development, the method comprising:

obtaining, by a processor, the plurality of user stories associated with a software for size estimation analysis;

computing, by the processor, a base size for each of the plurality of user stories based on at least one basic parameter, wherein the at least one basic parameter is indicative of a parameter affecting complexity associated with execution of a user story, and wherein the base size is indicative of a quantitative measure of complexity associated with execution of a user story with regard to the at least one basic parameter;

ascertaining, by the processor, an additional size for the plurality of user stories based on at least one additional parameter, wherein the at least one additional parameter is indicative of an environmental factor affecting delivery of the software, wherein the additional size is indicative of a quantitative measure of complexity associated with delivery of the software, which directly or indirectly impacts the execution of the plurality of user stories, and wherein the ascertaining comprises;

determining, by the processor, against each additional parameter, whether the impact of the each additional parameter is story-specific or sprint-specific;

determining, by the processor, an intermediate additional size for the plurality of user stories against each additional parameter, depending on whether the impact of the respective additional parameter is story-specific or sprint-specific; and

computing, by the processor, the additional size for the plurality of user stories based on the intermediate additional size determined against each additional parameter; and

determining, by the processor, an overall size of the plurality of user stories by consolidating the base size for each user story with the additional size for the plurality of user stories.

2. The method as claimed in claim 1, wherein determining the base size for each of the plurality of user stories comprises:

providing, by the processor, against each basic parameter, a plurality of options to a user for selection, wherein the plurality of options are indicative of choices to be opted for a user story, in response to a basic parameter;

allocating, by the processor, a predefined score, based on the selection of one of the plurality of options for the each basic parameter;

determining, by the processor, an intermediate base size of each user story, by consolidating predefined scores being allocated to the respective user story for basic parameters; and

computing, by the processor, the base size of each user story as an integer in a Fibonacci series which is closer to the intermediate base size.

3. The method as claimed in claim 1 further comprises:

ascertaining, by the processor, a consolidated effort for execution of the plurality of user stories, based on the overall size of the plurality of user stories and a pre-baselined delivery rate, wherein the pre-baselined delivery rate is indicative of a quantitative measure of capability of a team in terms of time required for execution of a user story;

determining, by the processor, a total capacity of the team for a pre-defined sprint, based on a number of members in the team and working hours of each member; and

allocating, by the processor, the plurality of user stories to the team for execution, based on the consolidated effort and the total capacity.

4. The method as claimed in claim 3 further comprising ascertaining a sprint velocity of the team based on an overall size of the plurality of user stories.

5. The method as claimed in claim 1 further comprises:

receiving, by the processor, from a user, when it is determined that the impact of an additional parameter is story-specific, an input indicative of a percentage of user stories, out of the plurality of user stories, to be impacted by the additional parameter; and

computing, by the processor, a score by which a total base size of the plurality of user stories is updated, wherein the score is computed based on the percentage of user stories to be impacted and a number of steps in Fibonacci series by which the base size of one user story is updated;

wherein the determining of the intermediate additional size for the plurality of user stories against the additional parameter is based on the computed score.

6. The method as claimed in claim 1 further comprises:

receiving, by the processor, from a user, when it is determined that the impact of an additional parameter is story-specific, an input indicative of known user stories that are impacted by the additional parameter;

computing, by the processor, a score by which the base size for each of the known user stories is updated, wherein the score is computed based on a number of steps in Fibonacci series by which the base size of one known user story is updated;

wherein the determining of the intermediate additional size for the plurality of user stories against the additional parameter is based on the computed score for the each known user story.

7. The method as claimed in claim 1 further comprises:

receiving, by the processor, from a user, when it is determined that the impact of an additional parameter is sprint-specific, a score by which a total base size for the plurality of user stories is updated;

wherein the determining of the intermediate additional size for the plurality of user stories against the additional parameter is based on the received score.

8. The method as claimed in claim 1, wherein the at least one basic parameter includes business rules, code refactoring, regression, design change, deployment management change, number of entities manipulated, primary data manipulation type, database trips, number of interfaces, performance, security, usability, and reliability.

9. The method as claimed in claim 1, wherein the at least one additional parameter includes roadmap clarity, product backlog management, reporting requirements, external dependencies, large data handling, third party component usage, new technology, sprint duration strictness, research items, and configurations.

10. An agile estimation system for size estimation of a plurality of user stories for agile software development, the agile estimation system comprising:

a processor; and

a base size estimation module, coupled to the processor, to,

obtain the plurality of user stories associated with a software for size estimation analysis; and

compute a base size for each of the plurality of user stories based on at least one basic parameter, wherein the at least one basic parameter is indicative of a parameter affecting complexity associated with execution of a user story, and wherein the base size is indicative of a quantitative measure of complexity associated with execution of a user story with regard to the at least one basic parameter;

an additional size estimation module, coupled to the processor, to ascertain an additional size for the plurality of user stories based on at least one additional parameter, wherein the at least one additional parameter is indicative of an environmental factor affecting delivery of the software, and wherein the additional size is indicative of a quantitative measure of complexity associated with delivery of the software, which directly or indirectly impacts the execution of the plurality of user stories, and wherein to ascertain the additional size, the additional size estimation module

determines against each additional parameter, whether the impact of the each additional parameter is story-specific or sprint-specific;

determines an intermediate additional size for the plurality of user stories against each additional parameter, depending on whether the impact of the respective additional parameter is story-specific or sprint-specific; and

computing the additional size for the plurality of user stories based on the intermediate additional size determined against each additional parameter; and

a work allocation module, coupled to the processor, to determine an overall size of the plurality of user stories by consolidating the base size for each user story with the additional size for the plurality of user stories.

11. The agile estimation system as claimed in claim 10, wherein the base size estimation module is coupled to the processor to,

provide, against each basic parameter, a plurality of options to a user for selection, wherein the plurality of options are indicative of choices to be opted for a user story with respect to the respective basic parameter;

allocate a predefined score, based on the selection of one of the plurality of options for the each basic parameter; and

determine an intermediate base size of each user story, by consolidating predefined scores allocated to the respective user story for basic parameters.

12. The agile estimation system as claimed in claim 11, wherein the base size estimation module is coupled to the processor to,

determine a stringent base size for each user story, wherein the stringent base size is an integer of Fibonacci series that is closest to and smaller than the intermediate base size of the respective user story;

determine a comfortable base size for each user story, wherein the comfortable base size is an integer of Fibonacci series that is closest to and larger than the intermediate base size of the respective user story;

compute the base size of each user story as one of the stringent base size and the comfortable base size which is closer to the intermediate base size of the respective user story.

13. The agile estimation system as claimed in claim 10, wherein the work allocation module is coupled to the processor to,

ascertain a consolidated effort for execution of the plurality of user stories, based on the overall size of the plurality of user stories and a pre-baselined delivery rate, wherein the pre-baselined delivery rate is indicative of a quantitative measure of capability of a team in terms of time required for execution of a user story;

determine a total capacity of the team for a pre-defined sprint, based on a number of members in the team and working hours of each member; and

allocate the plurality of user stories to the team for execution, based on the consolidated effort and the total capacity.

14. The agile estimation system as claimed in claim 13, wherein the work allocation module is coupled to the processor to ascertain a sprint velocity of the team based on an overall size of the plurality of user stories.

15. The agile estimation system as claimed in claim 10, wherein the additional size estimation module is coupled to the processor to,

receive, from a user, when it is determined that the impact of an additional parameter is story-specific, an input indicative of a percentage of user stories, out of the plurality of user stories, to be impacted by the additional parameter;

and

compute a score by which a total base size of the plurality of user stories is updated, wherein the score is computed based on the percentage of user stories to be impacted and a number of steps in Fibonacci series by which the base size of one user story is updated;

wherein the intermediate additional size for the plurality of user stories is determined against the additional parameter based on the computed score.

16. The agile estimation system as claimed in claim 10, wherein the additional size estimation module is coupled to the processor to,

receive, from a user, when it is determined that the impact of an additional parameter is sprint-specific, a score by which a total base size for the plurality of user stories is updated;

wherein the intermediate additional size for the plurality of user stories is determined against the additional parameter based on the received score.

17. The agile estimation system as claimed in claim 10, wherein the base size estimation module is coupled to the processor to provide a notification to a user when the base size of a user story, from amongst the plurality of user stories, is a predefined threshold value, wherein the notification provides an indication to the user to break the user story into multiple user stories.

18. The agile estimation system as claimed in claim 10, wherein the work allocation module is coupled to the processor to,

acertain a total effort for the plurality of user stories based on the base size for each user story and the additional size for the plurality of user stories and based on pre-baselined delivery rates;

determine a total capacity of a team for a predefined sprint, wherein the total capacity is determined based on a number of members in the team, duration of the predefined sprint, and working hours per day; and

determine a capacity utilization of the team based on a ratio of the total effort for the plurality of user stories and the total capacity of the team.

19. The agile estimation system as claimed in claim 10, wherein the at least basic parameter includes business rules, code refactoring, regression, design change, deployment management change, number of entities manipulated, primary data manipulation type, database trips, number of interfaces, performance, security, usability, and reliability.

20. A non-transitory computer-readable medium having embodied thereon a computer program for executing a method comprising:

obtaining, by a processor, the plurality of user stories associated with a software for size estimation analysis;

computing, by the processor, a base size for each of the plurality of user stories based on at least one basic parameter, wherein the at least one basic parameter is indicative of a parameter affecting complexity associated with execution of a user story, and wherein the base size is indicative of a quantitative measure of complexity associated with execution of a user story with regard to the at least one basic parameter;

ascertaining, by the processor, an additional size for the plurality of user stories based on at least one additional parameter, wherein the at least one additional parameter is indicative of an environmental factor affecting delivery of the software, wherein the additional size is indicative of a quantitative measure of complexity associated with delivery of the software, which directly or indirectly impacts the execution of the plurality of user stories, and wherein the ascertaining comprises;

determining, by the processor, against each additional parameter, whether the impact of the each additional parameter is story-specific or sprint-specific;

determining, by the processor, an intermediate additional size for the plurality of user stories against each additional parameter, depending on whether the impact of the respective additional parameter is story-specific or sprint-specific; and

computing, by the processor, the additional size for the plurality of user stories based on the intermediate additional size determined against each additional parameter; and

determining, by the processor, an overall size of the plurality of user stories by consolidating the base size for each user story with the additional size for the plurality of user stories.