SYSTEM AND METHOD FOR SOFTWARE APPLICATION LIFECYCLE MANAGEMENT

• US 20160253156A1
• Filed: 06/17/2015
• Published: 09/01/2016
• Est. Priority Date: 02/27/2015
• Status: Active Grant

• Current Assignee: Wipro Limited
• Original Assignee: Wipro Limited
• Inventors: Sripathan, Jayashree, Janki Rao, Uppada, Jose, Remil Paul, Sridhar, Kavitha
• Granted Patent: US 9,710,239 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 8/35 model driven
• CPC Class Codes: G06F 8/70 Software maintenance or management

Abstract

This disclosure relates generally to software application lifecycle management, and more particularly to system and method for software application lifecycle management using predictive models based on past similar software application deliveries. In one embodiment, a method is provided for software application lifecycle management. The method comprises gathering software application related information from a user, accessing outcomes of past similar software application deliveries from at least one of a knowledge repository and a learning repository based on the software application related information, deriving a set of models based on the outcomes of past similar software application deliveries, and providing options to the user for selection based on the set of models.

Claims

1. A method for application lifecycle management, the method comprising:

obtaining, by an application lifecycle management platform computing device, application related data and one or more outcomes of prior corresponding application deliveries from at least one of a knowledge repository or a learning repository based on the application related data;

generating, by the application lifecycle management platform computing device, a set of models based on the outcomes of the prior corresponding application deliveries; and

outputting, by the application lifecycle management platform computing device, one or more options for selection based on the set of models.

2. The method of claim 1, further comprising prompting, by the application lifecycle management platform computing device, a user to input a required outcome and a decision factor via a provided user interface, wherein the decision factor comprises a combination of input and desired output factors and requirement-to-requirement modeling, requirement-to-tasks modeling, requirement- to-test cases modeling, test cases-to-test cases modeling, or requirement-to-end-to- end solution modeling.

3. The method of claim 2, wherein the obtaining further comprises populating one or more keywords for selection based on the required outcome and the decision factor.

4. The method of claim 1, wherein the generating further comprises:

generating a search query based on the application related data; and

searching at least one of the knowledge repository or the learning repository for the outcomes of the prior corresponding application deliveries based on the search query.

5. The method of claim 1, wherein the generating further comprises deriving at least one of a risk or a confidence level for each of the set of models, the outputting further comprises providing each of the set of models along with the corresponding risk or confidence level, and each of the set of models is in a ready-to-use deliverable format.

6. The method of claim 1, further comprising triggering, by the application lifecycle management platform computing device, a new iteration or a new project in response to a selection of one or more of the options.

7. The method of claim 1, further comprising updating, by the application lifecycle management platform computing device, at least one of the knowledge repository or the learning repository based on intelligence generated from a selection of one or more of the options.

8. An application lifecycle management platform computing device, comprising at least one processor and a memory coupled to the processor which is configured to be capable of executing programmed instructions comprising and stored in the memory to:

obtain application related data and one or more outcomes of prior corresponding application deliveries from at least one of a knowledge repository or a learning repository based on the application related data;

generate a set of models based on the outcomes of the prior corresponding application deliveries; and

output one or more options for selection based on the set of models.

9. The application lifecycle management platform computing device of claim 8, wherein the processor coupled to the memory is further configured to be capable of executing at least one additional programmed instruction to prompt a user to input a required outcome and a decision factor via a provided user interface, wherein the decision factor comprises a combination of input and desired output factors and requirement-to-requirement modeling, requirement-to-tasks modeling, requirement-to-test cases modeling, test cases-to-test cases modeling, or requirement- to-end-to-end solution modeling.

10. The application lifecycle management platform computing device of claim 9, wherein the processor coupled to the memory is further configured to be capable of executing at least one additional programmed instruction to populate one or more keywords for selection based on the required outcome and the decision factor.

11. The application lifecycle management platform computing device of claim 8, wherein the processor coupled to the memory is further configured to be capable of executing at least one additional programmed instruction to:

generate a search query based on the application related data; and

search at least one of the knowledge repository or the learning repository for the outcomes of the prior corresponding application deliveries based on the search query.

12. The application lifecycle management platform computing device of claim 8, wherein the processor coupled to the memory is further configured to be capable of executing at least one additional programmed instruction to derive at least one of a risk or a confidence level for each of the set of models, the outputting further comprises providing each of the set of models along with the corresponding risk or confidence level, and each of the set of models is in a ready-to-use deliverable format.

13. The application lifecycle management platform computing device of claim 8, wherein the processor coupled to the memory is further configured to be capable of executing at least one additional programmed instruction to trigger a new iteration or a new project in response to a selection of one or more of the options.

14. The application lifecycle management platform computing device of claim 8, wherein the processor coupled to the memory is further configured to be capable of executing at least one additional programmed instruction to update at least one of the knowledge repository or the learning repository based on intelligence generated from a selection of one or more of the options.

15. A non-transitory computer readable medium having stored thereon instructions for managing a buffer cache comprising executable code which when executed by a processor, causes the processor to perform steps comprising:

obtaining application related data and one or more outcomes of prior corresponding application deliveries from at least one of a knowledge repository or a learning repository based on the application related data;

generating a set of models based on the outcomes of the prior corresponding application deliveries; and

outputting one or more options for selection based on the set of models.

16. The non-transitory computer readable medium of claim 15, further having stored thereon at least one additional instruction comprising executable code which when executed by the processor, causes the processor to perform at least one additional step comprising prompting a user to input a required outcome and a decision factor via a provided user interface, wherein the decision factor comprises a combination of input and desired output factors and requirement-to-requirement modeling, requirement-to-tasks modeling, requirement-to-test cases modeling, test cases-to-test cases modeling, or requirement-to-end-to-end solution modeling.

17. The non-transitory computer readable medium of claim 16, wherein the obtaining further comprises populating one or more keywords for selection based on the required outcome and the decision factor.

18. The non-transitory computer readable medium of claim 15, wherein the generating further comprises:

generating a search query based on the application related data; and

searching at least one of the knowledge repository or the learning repository for the outcomes of the prior corresponding application deliveries based on the search query.

19. The non-transitory computer readable medium of claim 15, wherein the generating further comprises deriving at least one of a risk or a confidence level for each of the set of models, the outputting further comprises providing each of the set of models along with the corresponding risk or confidence level, and each of the set of models is in a ready-to-use deliverable format.

20. The non-transitory computer readable medium of claim 15, further having stored thereon at least one additional instruction comprising executable code which when executed by the processor, causes the processor to perform at least one additional step comprising triggering a new iteration or a new project in response to a selection of one or more of the options.

21. The non-transitory computer readable medium of claim 15, further having stored thereon at least one additional instruction comprising executable code which when executed by the processor, causes the processor to perform at least one additional step comprising updating at least one of the knowledge repository or the learning repository based on intelligence generated from a selection of one or more of the options.