Agile estimation

• US 10,642,611 B2
• Filed: 09/17/2014
• Issued: 05/05/2020
• 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
• Primary Examiner(s): Bullock, Jr., Lewis A
• Assistant Examiner(s): Kabir, Mohammad H
• Application Number:
  US14/489,112
• Publication Number:
  US 20160077832A1
• Time in Patent Office: 2,057 Days
• 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 used in one or more agile software development projects assigned to multiple teams, 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 a basic parameter from a plurality of basic parameters assigned to each of the multiple teams, wherein the basic parameter comprises a pre-defined threshold value representing a characteristic contributing to complexity of each of the user stories, wherein computing the base size for each of the plurality of user stories comprises;

providing a plurality of options for the at least one basic parameter for each of the plurality of user stories, wherein the plurality of options are choices selected for a user story, in response to a basic parameter, and wherein the plurality of options provided as both values and text vary for each basic parameter from the plurality of basic parameters;

allocating a predefined score, based on selection of one of the plurality of options for the at least one basic parameter;

determining an intermediate base size of each user story, by consolidating predefined scores for each of the user stories for the at least one basic parameter;

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

computing the base size of each user story as one of the stringent base size and the comfortable base size closer to the intermediate base size of the respective user story, wherein a notification is provided to a user, if the computed base size is larger than the predefined threshold value as an indication to break each of the user story into multiple user stories;

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 comprises 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 impacting the execution of the plurality of user stories and wherein the ascertaining comprises;

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

determining, by the processor, received score as an intermediate additional size for the plurality of user stories against each additional parameter based on the user input score and 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;

determining, by the processor, an overall size of the plurality of user stories by consolidating the base size for and the additional size for each of the plurality of user stories corresponding to a predefined sprint for estimating a sprint velocity, wherein the sprint velocity is an estimation of number of story points delivered for each of the plurality of user stories in the predefined sprint, and wherein the overall size estimation is determined using a parametric analysis of the plurality of user stories by comparing the overall size of the plurality of user stories across sprints and one or more agile software development projects; and

ascertaining, by the processor, a consolidated effort for execution of the plurality of user stories based on the estimated sprint velocity along with a pre-baselined delivery rate, wherein the pre-baselined delivery rate is a quantitative measure of capability of the team in terms of time required for execution of the user story, the pre-baselined delivery rate is measured in person hours per story point (PH/ISP), and varied according to a technology of a user story.

2. The method as claimed in claim 1 further comprising ascertaining the sprint velocity of the team based on an overall size of the plurality of user stories.

3. 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.

4. 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.

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 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.

6. 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.

7. 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.

8. An agile estimation system used in one or more agile software development projects assigned to multiple teams, the agile estimation system comprising:

a processor;

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 a basic parameter from a plurality of basic parameters assigned to each of the multiple teams, wherein the basic parameter comprises a pre-defined threshold value representing a characteristic contributing to complexity of each of the user stories, wherein computing the base size for each of the plurality of user stories comprises;

providing a plurality of options for the at least one basic parameter for each of the plurality of user stories, wherein the plurality of options are choices selected for a user story, in response to a basic parameter, and wherein the plurality of options provided as both values and text vary for each basic parameter from the plurality of basic parameters;

allocating a predefined score, based on selection of one of the plurality of options for the at least one basic parameter;

determining an intermediate base size of each user story, by consolidating predefined scores for each of the user stories for the at least one basic parameter;

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

computing the base size of each user story as one of the stringent base size and the comfortable base size closer to the intermediate base size of the respective user story, wherein a notification is provided to a user, if the computed base size is larger than the predefined threshold value as an indication to break each of the user story into multiple user stories;

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 comprises 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 impacting 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 impact of the each additional parameter is story-specific or sprint-specific;

determines received score as an intermediate additional size for the plurality of user stories against each additional parameter based on the user input score and 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;

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 and the additional size for each of the plurality of user stories corresponding to a predefined sprint for estimating a sprint velocity, and wherein the sprint velocity is an estimation of number of story points delivered for each of the plurality of user stories in the predefined sprint, wherein the overall size estimation is determined using a parametric analysis of the plurality of user stories by comparing the overall size of the plurality of user stories across sprints and one or more agile software development projects; and

ascertain, by the processor, a consolidated effort for execution of the plurality of user stories based on the estimated sprint velocity along with a pre-baselined delivery rate, wherein the pre-baselined delivery rate is a quantitative measure of capability of the team in terms of time required for execution of the user story, the pre-baselined delivery rate is measured in person hours per story point (PH/ISP), and varied according to a technology of a user story.

9. The agile estimation system as claimed in claim 8, 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.

10. The agile estimation system as claimed in claim 8, wherein the work allocation module is coupled to the processor to ascertain the sprint velocity of the team based on an overall size of the plurality of user stories.

11. The agile estimation system as claimed in claim 8, 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.

12. The agile estimation system as claimed in claim 8, 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.

13. The agile estimation system as claimed in claim 8, wherein the work allocation module is coupled to the processor to,

ascertain 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 the 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.

14. The agile estimation system as claimed in claim 8, 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.

15. 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 a basic parameter from a plurality of basic parameters assigned to each of the multiple teams, wherein the basic parameter comprises a pre-defined threshold value representing a characteristic contributing to complexity of each of the user stories, wherein computing the base size for each of the plurality of user stories comprises;

providing a plurality of options for the at least one basic parameter for each of the plurality of user stories, wherein the plurality of options are choices selected for a user story, in response to a basic parameter, and wherein the plurality of options provided as both values and text vary for each basic parameter from the plurality of basic parameters;

allocating a predefined score, based on selection of one of the plurality of options for the at least one basic parameter;

determining an intermediate base size of each user story, by consolidating predefined scores for each of the user stories for the at least one basic parameter;

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

computing the base size of each user story as one of the stringent base size and the comfortable base size closer to the intermediate base size of the respective user story, wherein a notification is provided to a user, if the computed base size is larger than the predefined threshold value as an indication to break of the each of the user story into multiple user stories;

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 comprises 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 impacting 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, received score as an intermediate additional size for the plurality of user stories against each additional parameter based on the user input score and 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;

determining, by the processor, an overall size of the plurality of user stories by consolidating the base size for and the additional size for each of the plurality of user stories corresponding to a predefined sprint for estimating a sprint velocity, and wherein the sprint velocity is an estimation of number of story points delivered for each of the plurality of user stories in the predefined sprint, and wherein the overall size estimation is determined using a parametric analysis of the plurality of user stories by comparing the overall size of the plurality of user stories across sprints and one or more agile software development projects; and

ascertaining, by the processor, a consolidated effort for execution of the plurality of user stories based on the estimated sprint velocity along with a pre-baselined delivery rate, wherein the pre-baselined delivery rate is a quantitative measure of capability of the team in terms of time required for execution of the user story, the pre-baselined delivery rate is measured in person hours per story point (PH/ISP), and varied according to a technology of a user story.