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US-20260126994-A1 - GENERATING SOFTWARE FUNCTIONALITY DESCRIPTIONS USING GENERATIVE MACHINE LEARNING MODELS

US20260126994A1US 20260126994 A1US20260126994 A1US 20260126994A1US-20260126994-A1

Abstract

A computer-implemented method includes identifying development-related information within at least one functional architecture associated with a given item of software; generating at least one knowledge graph representing at least a portion of the development-related information; generating at least one prompt based at least in part on one or more portions of the at least one knowledge graph; generating one or more software functionality descriptions for at least a portion of the given item of software by processing the at least one prompt using at least one generative machine learning model; and performing one or more automated actions based at least in part on the one or more software functionality descriptions.

Inventors

  • Kun Yan Yin
  • Jing Zhang
  • Yuan Yuan Ding
  • Shi Yun Liang
  • Mehdi Charafeddine
  • Anthony Giordano
  • Yu Pan

Assignees

  • INTERNATIONAL BUSINESS MACHINES CORPORATION

Dates

Publication Date
20260507
Application Date
20241106

Claims (20)

  1. 1 . A computer-implemented method comprising: identifying development-related information within at least one functional architecture associated with a given item of software; generating at least one knowledge graph representing at least a portion of the development-related information; generating at least one prompt based at least in part on one or more portions of the at least one knowledge graph; generating one or more software functionality descriptions for at least a portion of the given item of software by processing the at least one prompt using at least one generative machine learning model; and performing one or more automated actions based at least in part on the one or more software functionality descriptions.
  2. 2 . The computer-implemented method of claim 1 , wherein identifying development-related information within at least one functional architecture associated with a given item of software comprises parsing information pertaining to one or more elements and one or more relationships within the at least one functional architecture.
  3. 3 . The computer-implemented method of claim 1 , wherein identifying development-related information within at least one functional architecture associated with a given item of software comprises processing one or more extensible markup language files associated with the at least one functional architecture.
  4. 4 . The computer-implemented method of claim 1 , wherein identifying development-related information within at least one functional architecture associated with a given item of software comprises processing one or more image features associated with the at least one functional architecture.
  5. 5 . The computer-implemented method of claim 1 , wherein performing one or more automated actions comprises automatically initiating one or more software development tasks for the given item of software based at least in part on the one or more software functionality descriptions.
  6. 6 . The computer-implemented method of claim 1 , wherein generating at least one knowledge graph comprises aligning two or more different descriptions of the at least a portion of the development-related information.
  7. 7 . The computer-implemented method of claim 1 , wherein generating at least one prompt comprises generating, based at least in part on the one or more portions of the at least one knowledge graph, one or more software-development-related requirements, one or more instructions for generating the one or more software functionality descriptions, and one or more formatting instructions.
  8. 8 . The computer-implemented method of claim 1 , wherein generating one or more software functionality descriptions for at least a portion of the given item of software comprises generating a first software functionality description, processing feedback related to the first software functionality description from at least one artificial intelligence agent, and generating a second software functionality description by modifying at least a portion of the first software functionality description based at least in part on one or more portions of the feedback.
  9. 9 . The computer-implemented method of claim 1 , wherein performing one or more automated actions comprises automatically training at least a portion of the at least one generative machine learning model using feedback related to the one or more software functionality descriptions.
  10. 10 . The computer-implemented method of claim 1 , wherein performing one or more automated actions comprises automatically transmitting the one or more software functionality descriptions to one or more of at least one software-development-related system and one or more software-development-related users.
  11. 11 . A computer program product comprising: one or more computer-readable storage media; and program instructions stored on the one or more computer-readable storage media to perform operations comprising: identifying development-related information within at least one functional architecture associated with a given item of software; generating at least one knowledge graph representing at least a portion of the development-related information; generating at least one prompt based at least in part on one or more portions of the at least one knowledge graph; generating one or more software functionality descriptions for at least a portion of the given item of software by processing the at least one prompt using at least one generative machine learning model; and performing one or more automated actions based at least in part on the one or more software functionality descriptions.
  12. 12 . The computer program product of claim 11 , wherein identifying development-related information within at least one functional architecture associated with a given item of software comprises parsing information pertaining to one or more elements and one or more relationships within the at least one functional architecture.
  13. 13 . The computer program product of claim 11 , wherein identifying development-related information within at least one functional architecture associated with a given item of software comprises processing one or more extensible markup language files associated with the at least one functional architecture.
  14. 14 . The computer program product of claim 11 , wherein identifying development-related information within at least one functional architecture associated with a given item of software comprises processing one or more image features associated with the at least one functional architecture.
  15. 15 . The computer program product of claim 11 , wherein performing one or more automated actions comprises automatically initiating one or more software development tasks for the given item of software based at least in part on the one or more software functionality descriptions.
  16. 16 . A computer system comprising: a processor set; one or more computer-readable storage media; and program instructions stored on the one or more computer-readable storage media to cause the processor set to perform operations comprising: identifying development-related information within at least one functional architecture associated with a given item of software; generating at least one knowledge graph representing at least a portion of the development-related information; generating at least one prompt based at least in part on one or more portions of the at least one knowledge graph; generating one or more software functionality descriptions for at least a portion of the given item of software by processing the at least one prompt using at least one generative machine learning model; and performing one or more automated actions based at least in part on the one or more software functionality descriptions.
  17. 17 . The computer system of claim 16 , wherein identifying development-related information within at least one functional architecture associated with a given item of software comprises parsing information pertaining to one or more elements and one or more relationships within the at least one functional architecture.
  18. 18 . The computer system of claim 16 , wherein identifying development-related information within at least one functional architecture associated with a given item of software comprises processing one or more extensible markup language files associated with the at least one functional architecture.
  19. 19 . The computer system of claim 16 , wherein identifying development-related information within at least one functional architecture associated with a given item of software comprises processing one or more image features associated with the at least one functional architecture.
  20. 20 . The computer system of claim 16 , wherein performing one or more automated actions comprises automatically initiating one or more software development tasks for the given item of software based at least in part on the one or more software functionality descriptions.

Description

COPYRIGHT NOTICE A portion of the disclosure of this patent document contains material which is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent file or records, but otherwise reserves all copyright rights whatsoever. BACKGROUND The present application generally relates to information technology, to software development, to generative machine learning models, to artificial intelligence agents that interact with generative machine learning models, and to utilizing resources of generative machine learning models to assist with software development tasks. SUMMARY In at least one embodiment, an example computer-implemented method can include identifying development-related information within at least one functional architecture associated with a given item of software, and generating at least one knowledge graph representing at least a portion of the development-related information. The method also includes generating at least one prompt based at least in part on one or more portions of the at least one knowledge graph, and generating one or more software functionality descriptions for at least a portion of the given item of software by processing the at least one prompt using at least one generative machine learning model. Further, the method includes performing one or more automated actions based at least in part on the one or more software functionality descriptions. Another embodiment of the invention or elements thereof can be implemented in the form of a computer program product tangibly embodying computer-readable instructions which, when implemented, cause a computer to carry out a plurality of method steps, as described herein. Furthermore, another embodiment of the invention or elements thereof can be implemented in the form of a system including a memory and at least one processor that is coupled to the memory and configured to perform noted method steps. Yet further, another embodiment of the invention or elements thereof can be implemented in the form of means for carrying out the method steps described herein, or elements thereof; the means can include hardware module(s) or a combination of hardware and software modules, wherein the software modules are stored in a tangible computer-readable storage medium (or multiple such media). These and other objects, features and advantages of the present invention will become apparent from the following detailed description of illustrative embodiments thereof, which is to be read in connection with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a diagram illustrating example system architecture for automatically generating software functionality descriptions, according to an example embodiment of the invention; FIG. 2 is a diagram illustrating an example architecture graph parser workflow, according to an example embodiment of the invention; FIG. 3 is a diagram illustrating an example workflow for aligning different entity descriptions, according to an example embodiment of the invention; FIG. 4 is a diagram illustrating an example workflow for generating a user story, according to an example embodiment of the invention; FIG. 5 shows example pseudocode for generating a user story prompt template in an illustrative embodiment; FIG. 6 is a flow diagram illustrating techniques according to an example embodiment of the invention; and FIG. 7 is a diagram illustrating a computing environment in which at least one embodiment of the invention can be implemented. DETAILED DESCRIPTION As described herein, at least one embodiment includes automatically generating software functionality descriptions (also referred to herein as user stories) based at least in part on one or more functional architecture graph and requirements using at least one generative machine learning model (e.g., at least one large language model (LLM)). As used herein, a user story generally refers to a description of software functionality used in software development, wherein such a description can include a focusing on, for example, one or more user needs, one or more values, etc. More specifically, descriptions of software functionality used in software development can help development teams better understand and meet user needs. However, in many conventional software development approaches, such descriptions are often limited to individual portions of the given software and lack relevant knowledge of comprehensive system architecture, thus leading to inaccurate and/or error-prone descriptions and resultant software outputs. According to an aspect of the invention, there is provided a computer system, a computer program product, and a computer-implemented method for performing operations including identifying development-related information within at least one functional architecture associated with a given