DERIVING PROCESS MODELS FROM NATURAL LANGUAGE USE CASE MODELS

• US 20110239183A1
• Filed: 03/25/2010
• Published: 09/29/2011
• Est. Priority Date: 03/25/2010
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: SINHA, AVIK, PARADKAR, AMITKUMAR M.
• Granted Patent: US 8,949,773 B2
• Time in Patent Office: Days
• US Class Current: 717/104
• CPC Class Codes: G06F 8/10 Requirements analysis; Specification techniques
• CPC Class Codes: G06F 8/34 Graphical or visual programming

Abstract

One or more process models from natural language use case models are derived, for example, by creating, using a processor, an in-memory model of a use case from information in natural language text describing the use case; transforming the in-memory model into a process model in predetermined modeling notation; and generating a selected business process model using the process model.

Claims

1. A method for deriving one or more process models from natural language use case specifications, comprising:

creating, using a processor, an in-memory model of a use case from information in natural language text describing the use case;

transforming the in-memory model into a process model in predetermined modeling notation; and

generating a business process model representing an end to end business scenario using the process model.

2. The method of claim 1, wherein a plurality of process models are transformed from a plurality of in-memory models, and the generation of business process model includes selectively combining the plurality of process models.

3. The method of claim 1, further including:

automatically updating the in-memory model of the use case in response to the process model or the selected business process model or both being changed.

4. The method of claim 3, wherein the automatically updating further includes automatically updating the natural language text describing the use case.

5. The method of claim 4, wherein the automatically updating includes one or more of:

transforming an addition of one or more of an activity or a sub-process into creation of an action in the natural language text;

transforming an addition of a gateway into creation of an exception in the natural language text;

transforming a deletion of an action into deletion of the action in the natural language text;

transforming a deletion of a gateway into deletion of the action from the text;

transforming renaming of an element by renaming all instances of the element in the natural language text;

or

or combinations thereof.

6. The method of claim 1, further including:

automatically retransforming the in-memory model of the use case to an updated process model in response to the in-memory model being changed.

7. The method of claim 1, further including:

repeating the steps of creating and transforming in response to the natural language text being updated.

8. The method of claim 1, wherein the predetermined modeling notation is BPMN.

9. The method of claim 1, wherein the step of transforming includes:

creating a pool, the pool being initially empty, for each actor in the use case, wherein a start event and one or more end events are associated with said each actor, creating a start event in the pool representing an initiating actor of a first action in the use case and processing each sentence of the use case in sequence to create a flow of activities in the process model;

creating connectors according to a sequence of activities in the in-memory model; and

linking final activity to an end event.

10. The method of claim 9, wherein the step of transforming further includes:

Transforming one or more exceptions in the in-memory model into gateway-s of the process model.

11. The method of claim 1, wherein the step of generating a selected business process model includes using def-use analysis of execution paths of the process model.

12. A system for deriving one or more process models from natural language use case models, comprising:

a processor operable to create an in-memory model of a use case from information in natural language text describing the use case, the processor further operable to transform the in-memory model into a process model in predetermined modeling notation, and the processor further operable to generate a selected business process model using the process model; and

a user interface module operable to guide a user to create the in-memory model, generate the selected business process model, edit the natural language text, and edit the selected business process model and edit the in-memory model.

13. The system of claim 12, wherein the processor is further operable to automatically update the in-memory model of the use case in response to the process model or the selected business process model or both being changed.

14. The system of claim 13, wherein the processor is further operable to automatically update the natural language text describing the use case.

15. A computer readable storage medium storing a program of instructions executable by a machine to perform a method of deriving one or more process models from natural language use case models, comprising:

creating, using a processor, an in-memory model of a use case from information in natural language text describing the use case;

transforming the in-memory model into a process model in predetermined modeling notation; and

generating a selected business process model using the process model.

16. The computer readable storage medium of claim 15, wherein a plurality of process models are transformed from a plurality of in-memory models, and the generation of business process model includes selectively combining the plurality of process models.

17. The computer readable storage medium of claim 15, further including:

automatically updating the in-memory model of the use case in response to the process model or the selected business process model or both being changed.

18. The computer readable storage medium of claim 17, wherein the automatically updating further includes automatically updating the natural language text describing the use case.

19. The computer readable storage medium of claim 18, wherein the automatically updating includes one or more of:

transforming an addition of one or more of an activity or a sub-process into creation of an action in the natural language text;

transforming an addition of a gateway into creation of an exception in the natural language text;

transforming a deletion of an action into deletion of the action in the natural language text;

transforming a deletion of a gateway into deletion of the action from the text;

transforming renaming of an element by renaming all instances of the element in the natural language text;

or

or combinations thereof.

20. The computer readable storage medium of claim 15, further including:

automatically retransforming the in-memory model of the use case to an updated process model in response to the in-memory model being changed.

21. The computer readable storage medium of claim 20, wherein the step of transforming further includes:

transforming one or more exceptions in the in-memory model into gateway-s of the process model.

22. The computer readable storage medium of claim 20, wherein the step of generating a selected business process model includes using def-use analysis of execution paths of the process model.

23. The computer readable storage medium of claim 15, further including:

repeating the steps of creating and transforming in response to the natural language text being updated.

24. The computer readable storage medium of claim 15, wherein the step of transforming includes:

creating a pool, the pool being initially empty, for each actor in the use case, wherein a start event and one or more end events are associated with said each actor, creating a start event in the pool representing an initiating actor of a first action in the use case and processing each sentence of the use case in sequence to create a flow of activities in the process model;

creating connectors according to a sequence of activities in the in-memory model; and

linking final activity to an end event.